Anyone can train a model that looks good in a demo, but few can keep it running when real traffic and messy data hit. At Trango Tech, we don’t just build models; we ship them, monitor them, and keep them working long after launch, which is why you should hire machine learning engineers from us.
ravi@trango
A machine learning engineer is a specialized software engineer who researches, builds, and deploys predictive artificial intelligence models. They act as a bridge between data science and software engineering by converting theoretical models into production-ready software. Trango tech places highly experienced senior MLEs in US/EU teams in 72 hours, with a 5-day SLA (service level agreement). We also provide them with a predefined pricing table, ranging from $48/hour in India to $165/hour for US Senior. Pod starts at $9K/ week. We hire machine learning engineers not based on Kaggle pedigree.
Machine learning developers do not always fail because they cannot train the model. But because they don’t have any experience in shipping a model that stayed reliable for one year.
If you’re looking to hire ML developers, keep in mind that the math has changed now. Average AI engineer salary hit $206K in 2025 (Signify Technology), ML/AI hiring grew 88% YoY, and the US Bureau of Labor Statistics projects 23% growth through 2032. Demand is far outpacing supply; that's why most ML engineer hiring goes wrong. Five things you must know before hiring machine learning developers.
An ML engineer and a data scientist are two different things. A data scientist turns raw, unstructured data into actionable strategies. On the other hand, the ML engineer builds and deploys those statistical models into real-world applications. So, your hiring decision can make or break your product.
Hire machine learning experts based on their experience and hands-on expertise, not someone whose entire portfolio is Jupyter notebooks or Kaggle competitions. Production ML requires SQL, containerization, drift detection, eval harnesses, and an opinion on retraining cadence, not just XGBoost knowledge.
Every other agency claims to have “top 1%” talent, but what if the engineer doesn’t work out?. Our statement of work (SOW) promises a 5-day replacement in writing, not verbally. Toptal replaces it in 2 weeks, whereas most agencies say, “We’ll figure it out.”
A senior ML engineer in the US costs $150–$240/hr. The same skill in Central/Eastern Europe is $80–$110/hr; in India it’s $48–$65/hr. See our published rate matrix below. The watch-out isn’t quality; it’s overlap hours, which we map explicitly per region.
A $206K in-house senior costs ~$288K loaded (benefits, equity, recruiter, payroll tax). That’s ~$138/hr if they actually work 2,080 hrs. A Trango pod-blended senior at $115/hr with no recruiter fee, no payroll burden, and a 5-day replacement clause works out differently. Run our TCO calculator with your real inputs.
In 2 minutes, figure out which seniority level and how many engineers you actually need. No spam, no “Hi, just following up” cadence after.
Run Sizing QuizThe #1 hiring mistake we see is that you need an ML engineer but hired a data scientist or vice versa. They are adjacent but not interchangeable. So, make sure to match the right role to the work, or you’ll be hiring again in a few months.
Hire machine learning engineers when you need someone to handle the entire AI pipeline, from data processing to model deployment and production updates. Their success is measured by real-world business results, not just high-accuracy scores.
Hire when you need to decide whether to build complex models. They design experiments, analyze data, and create reports and dashboards. They generate insights rather than writing the actual software code that runs in an application.
Hire an MLOps engineer when you have multiple models in production and need the platform around them. They are responsible for feature stores, CI/CD for models, observability, and drift response. They are often promoted from senior ML engineers with strong DevOps instincts.
Hire when standard models won’t hit your accuracy bar, and you can fund weeks of exploration with uncertain payoff. Typically PhD with publications. Often pairs with an ML engineer who productionizes their work.
Need a walk-through on which role fits? A principal, not a recruiter, replies within 1 business day. NDA available before the first call.
You can get ML engineering capacity into your business in three ways. We win in one column; the rest is the honest map of when each option makes sense. Including the marketplaces and staffing firms a smart buyer should consider before signing custom paperwork.
Already deep in one of these options? Tell us which one and we’ll send a paid-for second opinion on whether to scale, augment, or migrate.
Already deep in one of these options? Tell us which one and we’ll send a paid-for second opinion on whether to scale, augment, or migrate.
Production ML isn’t one job; it's a 7-stage pipeline. We map roles to stages so you hire the narrowest senior for the work that's actually broken, not a full-stack generalist who does each stage at 60%.
A senior ML engineer who’s spent years mastering tabular classification isn’t automatically your best bet for real-time computer vision. That’s why we sort engineers by the production patterns they’ve actually shipped, not by resume buzzwords. So that you can hire a dedicated machine learning developer.
Train AI models. Common use cases include predictive analytics, risk assessment, and personalization. Traditionally, it relies on statistical algorithms and Gradient Boosted Decision Trees (GBDTs), including XGBoost, LightGBM, and CatBoost, for structured data.
Prediction of future values based on historical, time-stamped data. Extract trends and seasonality, engineer temporal features, and deploy algorithms such as long short-term memory (LSTM) networks, transformers, and gradient boosting (e.g., XGBoost, LightGBM).
Building systems that process, analyze, and interpret visual data from images and videos. Core capabilities include image classification, object detection & tracking, semantic & instance segmentation, facial recognition, and 3D reconstruction.
Spans a spectrum of classical statistical methods, deep learning architectures, and modern foundation models. Text preprocessing & normalization, feature extraction, classical modeling, and syntactic analysis.
It involves processing large data sets, engineering predictive features, and employing multi-stage architectures (candidate generation, scoring, & re-ranking) to deliver highly personalized experiences at scale.
Identifying rare, unusual, or suspicious data points that deviate from expected normal behavior. This allows organizations to proactively detect issues such as credit card fraud, cyberattacks, equipment failures, and network outages.
CI/CD/CT pipeline automation, model serving, containerization & orchestration. Implementing safe rollouts using techniques like A/B testing and shadow deployments. They also implement monitoring for data drift and concept drift.
To build scalable ML models, engineers must master the foundational data stack. It includes Apache Spark, dbt, Apache Airflow, and Feast. 60% of ML work that’s actually data engineering. We hire engineers who own this end-to-end.
Need a specialization not listed? We have ~24 more engineers in less-public specialties (recommender re-rankers, RL, federated learning, on-device). Just ask.
Yet here’s another hiring mistake: Over-hiring means you’re appointing 5 engineers when 2+1 MLOPs would shape faster, or under-hiring (1 senior when scope needs a pod). Answer 5 questions, and we’ll give you a written recommendation. Output reappears after the contact step.
We follow the same process for every engagement. The only difference is which phases your situation needs and how deep each goes. Most clients hit production cutover by day 60.
We begin by having a 30-minute call with a principal, not a recruiter. As a result, we give written role recommendations, pod size, target rate band, and an EU AI Act exposure note. We don’t provide pitch decks or follow a conventional multi-step outreach sequence.
# scoping_output.md
role: "sr_ml_engineer"
pod_size: 2
rate_band: "$95-$135/hr"
ai_act_exposure: "limited risk"Shortlist 2- 3 engineers from our team of specialized engineers within 72 hours. The choice is yours; they come with GitHub/HF/Kagge links + last shipped use case+ reference call available.
# shortlist.json
{
candidates: [3],
sla_hit_h: 62,
refs_available: true
}We offer a one-week paid trial at reduced rates. A newly hired engineer fully integrates into your team’s workflow and ships a real ticket. You need to provide a formal evaluation report. If you feel it's not a good fit, we offer free replacements, no questions asked.
# trial_outcome.yml
ticket: "feature_store_v0"
status: "shipped"
review: "keep"Now we are onboard by signing the SOW and transferring the IP to you. And engineers will be in your repository, your standup, and your Slack. We provide an embedded delivery lead at no extra charge to handle blocker triage.
# onboarding.sh
grant_repo_access ravi@trango
add_to_slack #ml-eng, #standup
assign_delivery_lead --no-chargeEngineer meets ships against an explicit Day 30/60/90 milestone calendar, detailed in §13 below . We don’t restate it here; the calendar is its own artifact with target deliverables, eval thresholds, and the gates that each milestone meets.
# deploy → see §13 Day 30/60/90 calendar
ref: "#days-h"
gates: ["d30_staging", "d60_cutover", "d90_retrain"]Pod adds in 5 business days. Engineers stay or rotate to other clients (your call). Drift SLA active for the first 90 days post-cutover regardless of engagement extension.
# scale_plan.yml
pod_expansion_sla_d: 5
drift_sla_d: 90
retain_or_rotate: "client choice"Want this process mapped to your specific timeline? 30 minutes with a principal. We’ll walk through it against your week-0 calendar.
The process is the same as we’ve discussed above. Here are four ways, depending on whether you want to hire a single ML developer, a team, a diagnostic burst, or an embedded long-term partner.
One experienced & senior ML engineer embedded with your team. If you think that the hired ML engineer is a misfit, we will handle the replacement, contracts, and pay. Best when you have ML leadership in-house only.
We have 2-4 engineers + an embedded delivery lead. Our ML engineers come with eval harness setup, drift telemetry, and IP-clean SOW. Most common picks for production-first builds.
This model has a fixed fee for a 4-week engagement. It has 2 engineers + lead, and as an output you’ll get a production-readiness audit, sizing recommendation, and build plan with confidence intervals. You can walk away after.
6+ months pod with rotation rights. Engineers stay or swap based on your roadmap. It is best when the ML pipeline has 5+ modes in flight at any time.
Most agencies trick you by hiding their rates and claiming “whatever you’ll pay”. But we’re very transparent about it. Below are the same bands we anchor in the SOW. Pod-blended individual specialist premiums noted at the bottom.
| role × region | USA · onshore | LATAM · nearshore | CEE · E.Europe | India · offshore |
|---|---|---|---|---|
| ML Engineer (Sr.) |
$135–$165
/hr
|
$72–$105
/hr
|
$88–$118
/hr
|
$48–$72
/hr
|
| MLOps Engineer (Sr.) |
$145–$185
/hr
|
$78–$115
/hr
|
$95–$130
/hr
|
$52–$78
/hr
|
| Data Scientist (Sr.) |
$115–$140
/hr
|
$65–$92
/hr
|
$78–$105
/hr
|
$42–$62
/hr
|
| Research Scientist |
$185–$240
/hr
|
$110–$155
/hr
|
$125–$175
/hr
|
$72–$105
/hr
|
Note: Pod-blended rates are 10–15% below the top of band when you take 3+ engineers from one region. Specialist premiums: CV +20%, real-time inference +25%, regulated/SR 11-7 +15%. Rates refreshed quarterly and last updated .
Want pod-blended rates for your specific role + region mix? Run the TCO calculator below for full 12-month math.
Run TCO CalculatorAI/ML hiring grew 88% YoY in 2025, with US AI job postings accounting for 29.4% of global demand. Hiring an ML engineer in 2026 is a candidate-driven market.
herohunt.ai · Phaidon International, 2026Average AI engineer salary hit $206K in 2025, up $50K YoY. With fully-loaded benefits + equity + recruiter fees, in-house TCO runs $260K–$310K per senior MLE.
Signify Technology, 2026 Salary Benchmarks23% projected growth in ML engineer roles through 2032, outpacing average across all occupations. The talent shortage is structural, not cyclical.
US Bureau of Labor StatisticsUS freelance ML rates: $70–$600/hr with median at $118–$195. Specialists (CV, MLOps, distributed) command $275–$450/hr. Nearshore agencies: $27–$82/hr.
secondtalent.com · goLance 2026~32% of new ML engineering hires don’t make it to the 90-day mark. Median time to backfill an unsuccessful senior ML hire is 4.6 months the strongest argument for a contractual replacement clause over “best effort.”
LinkedIn Talent Insights + Toptal Tech Hiring Report 2025$113B ML market in 2026 → $503B by 2030. Demand for engineers is going up, not stabilizing. Locking in pre-vetted capacity in 2026 is a multi-year hedge.
Phaidon International, ML Market Outlook 2026We don’t make baseless commitments; if you want to hire machine learning engineers from us, review the following milestones we commit to in writing. If we miss a milestone, you can exit at the milestone gate with no further obligation.
By day 30: you’ll have a working prototype built on your actual historical information. It is trained on your real data. The model is then hosted in a pre-production environment, stable enough to be tested by your internal team. Eval harness wired against a golden set of 500- 1000 cases. Even stakeholders can see predictions in your sandbox dashboard.
By day 60: the model is deployed to real users. The code is officially in the main software environment, but the feature flag gates access. Drift telemetry baselined. A/B vs. control measure for business impact: the team measures key business metrics to see whether the new model actually improves. After analyzing the 60-day results, the team makes a final call.
By day 90: the system observes your model's performance on real-world production data for 30 days. Once the baseline is established, the model is refreshed; this happens automatically if the statistical threshold for data drift is crossed, or on a schedule (e.g., every 30 days) to incorporate new real-world data into the AI. By day 90, the service level agreement (SLA) is officially enforced. An audit trail is published.
Want this milestone calendar mapped to your project? 30 minutes with a principal. We’ll back-fill from your target date.
We have listed the actual categories of our ML engineers below. Each one of them has a scoring rubric. Engineers who fail any single category don’t make the pool, no matter how good their resume.
Machine learning experts provided with missing values, outliers, schema drift, and label noise must be able to identify issues, document remediation strategy, and quantify confidence.
It involves training three candidate models against a golden set. Engineers must calculate metric scores and 95% confidence intervals, determine which model is the most consistent, and recommend a baseline with a clear trade-off.
Deploy the machine learning baseline as a production-ready Fast API service inside a Docker container. Guarantees that your application includes robust error handling to reject malformed inputs gracefully and implements structured logging to capture telemetry for reliable monitoring.
Must be able to document the model, including training data, intended use, limitations, bias considerations, and performance breakdown by segment.
Should capture every inference request, the model’s prediction, and the actual outcome. For the deployed model, design a monitoring plan. Monitor thresholds, alert routing, and retraining triggers. Must be implementable in production, not just theoretical.
Serving 10M predictions per day costs roughly $30 to $300, depending on your setup. Trade-offs across batch vs. real-time, GPU vs. CPU, cloud vs. on-premises. Show the math.
Want the full assessment rubric? We’ll send the complete scoring template; you can use it for your own internal hires too.
Before booking a call to hire a machine learning engineer, you must inspect these ten things. An engineer’s actual work on developer platforms demonstrates their real skill, as shown by GitHub, Hugging Face, and Kaggle profiles. A 10-point audit is a quick 15-minute diagnostic check to evaluate the health of a project, website, or business strategy.
Check: Public contribution graph. Why it matters: senior MLEs ship to open source, fork tools, or maintain personal reports. A flat green graph signals either heavily regulated work (legit, ask) or atrophied skills (not legit).
Check: GitHub language bar on top repos. Why it matters: production ML lives in .py modules + containers, not .ipynb. 100% notebooks = never had to make code maintainable.
search: the repo for Dockerfile. Why it matters: if they’ve never containerized a model, deployment isn’t a competency. That work falls to your platform team.
requirements.txt / pyproject.toml / poetry.lockCheck: open 3 ML-related repos. Why it matters: if reproducibility isn’t pinned, the work was one-off. Engineers who’ve had to debug a production environment never skip dependency locking again.
Check: the READMEs of the top 3 model repos. Why it matters: senior MLEs document metrics, baseline, and known limitations. “Run pip install and that’s it” means the model was never evaluated rigorously enough to write about.
Check: huggingface.co/[username]. Why it matters: HF is where production ML engineers publish reproducible work. Empty HF + active GitHub is a signal they shipped private work, not open ones; defensible but worth probing.
Check: Kaggle profile + GitHub side by side. Why it matters: Kaggle teaches model tuning under fixed data. Production teaches orthogonal skills: data pipelines, drift response, and stakeholder management. Kaggle ranking is not a substitute.
Check: count distinct ML libraries in their top 5 repos vs the resume list. Why it matters: the honest signal is deep in 3–5 things, exposure to 10 more. A 50-framework resume + 3-framework GitHub = buzzword cram.
Check: Google “site:medium.com [name]” / “site:arxiv.org [name]” / YouTube. Why it matters: senior MLEs explain their work publicly. Silence isn’t fatal (some companies forbid it), but it removes a signal of competence.
model_card.md / no eval harness commitsCheck: grep repos for “model card”, “eval”, “benchmark”. Why it matters: model cards and eval harnesses are the artifacts that distinguish production thinkers from notebook tinkerers. Absence: candidate hasn’t worked on systems where stakeholders ask, “How do we know this is safe?”
Want the audit checklist as a 1-page PDF? We’ll send the file our delivery leads use when shortlisting candidates.
You can use these questions in your own interviews. We’ve also mentioned what a good question looks like below each question and what a bad answer signals. We can also send you an extended rubric of 10 more questions; just drop us an email.
Diagnoses which kind of drift (input, concept, label). Pulls production telemetry first. Compares feature distributions to training. Has a retraining trigger threshold. Discusses whether to retrain or roll back.
Jumps straight to “retrain on more data.” Doesn’t mention monitoring. Doesn’t distinguish drift types. Has never actually handled this in production.
Calibration: How reliably a model’s predicted probabilities match real-world frequencies. If the model says there is an 80% chance of rain, it's right 80% of the time. Accuracy: Measures how often a model predicts the correct label. Talks about reliability diagrams, Brier scores. Calibration matters more in credit, healthcare, anywhere probability is used downstream.
Confusing the two concepts by saying they’re basically the same thing. Doesn’t know when calibration matters more than accuracy. We haven’t used a reliability diagram.
We ask you about the domain drift speed first. Talks about both scheduled (weekly/monthly). Knows that eval gate must pass before redeploy. Has opinions on champion/challenger.
We retrain after every quarter. Don't ask about the use case. Don’t mention eval gate anywhere; treat retraining as one-time rather than continuous.
Knows the calculations: batch = simpler infrastructure, lower cost, higher staleness. Online = lower latency, harder infra, more $/predictions. Therefore, engineers must balance the speed they need for their data with their budget to choose the best feature store decision framework for production.
Online is better. Don't know that batch is simpler. No views about feature freshness vs SLA. Never deployed batch.
They must include the model’s intended use, a summary of the training data, performance by segment, limitations, boundaries, and the last training data. Must be transparent about the stakeholder and regulatory trail (EU AI Act, SR 11-7).
A vague answer that just says “documentation”. Doesn’t clarify the standard sections. Treats it as a compliance checkbox rather than a stakeholder tool.
Tell a specific story with metrics. Honestly communicate their contribution to the mistake. Clear lesson learned and explicit change in how they work now. And it will be a plus point if they mention the prevention process they put in place after.
I don’t remember I made any mistake. Generic story without numbers. Blames other teammates, data, or management. No actual lesson learned.
Our average AI engineer salary, which doesn’t include benefits, equity, recruiter fees, payroll tax, or the 4-month ramp-up. This calculator calculates both in-house FTE and Trango pod for your specific seniority + region + duration. Output appears after the contact step.
We don't make you sign without showing you the contract terms. We publish load-bearing clauses on the page. These are directly from our SOW, just reworded, and we are ready to provide the full version on request.
# §3.1 — IP Vesting
All code, models, training pipelines, eval
harnesses, and architecture artifacts produced
under this engagement vest in CLIENT
on payment, retroactive to commit date.
Trango retains no license-back.# §7.4 — Data Disposition
On engagement close, Client data, embeddings,
feature stores, and derived artifacts are deleted
with written certification within 14 days.
Engineer access revoked within 4 hours
of termination notice.# §4.2 — Engineer Replacement
If a placed engineer is not the right fit
for any reason, Trango will provide a qualified
replacement within 5 business days
at no additional charge.
No questions about the reason.# §6.1 — Use Restrictions
Trango will not use Client data to train
models for any other client, internal R&D, or
portfolio assets. Cloud provider settings
explicitly turn off training.
Engineer signs individual NDA before access.There’s no vendor lock-in or preferred stack sales sheet. When you hire machine learning engineers from us, rest assured that they choose the right tool for each use case. Below, we have mentioned specific technology stacks our ML engineers are actively relying on and deploying.
Most agencies try to get you to hire ML developers from the region with the most engineers on the bench. But we encourage you to choose the region that best matches your overlap hours, regulatory posture, and budget. Below is a description of what each region is actually good for and what to watch out for.
| Region | Overlap with US Eastern | Compliance fit | Best for | Watch out for |
|---|---|---|---|---|
| USA (Onshore) | Full-day | SR 11-7, HIPAA, FedRAMP, SOC 2 — full US data residency | Regulated industries, W2 required, security-cleared work, US-citizen-only contracts | Cost. 2.5–3x nearshore. See published rate matrix → — worth it when compliance demands it. |
| LATAM (Nearshore) | 5–7 hours/day | LGPD (Brazil), data-export-friendly, US-court-enforceable contracts via DR/MX/CO entities | Real-time collaboration, Spanish/Portuguese product, time-zone-aligned standups | English fluency variance. Vet for technical English, not just conversational. |
| CEE (Eastern Europe) | 3–5 hours/day | GDPR-native, EU AI Act-ready, ISO 27001 prevalence strong data-protection defaults | PhD + research backgrounds (Poland, Romania), GDPR-native build, research-heavy work | Ukraine geopolitical risk we route via Poland/Romania for engagement continuity. |
| India (Offshore) | 1–3 hours/day | DPDP Act 2023, SOC 2 common at scale, GDPR-mappable via sub-processor agreements | Cost-sensitive scale, 24/7 follow-the-sun ops, data engineering at volume | Overlap discipline matters. Insist on 3-hour overlap blocks, not async-only. |
Rates by region: we keep this section about strategy, not pricing. For region-by-role hourly bands, see the published rate matrix above →
We asked the last six clients who chose Trango Tech over a marketplace, staffing firm, or in-house hire what tipped the decision. Their remarks about us are below, with their titles, identified use cases, and names withheld by request.
The best thing about Trango Tech that made us close the deal was their SOW. They mentioned a replacement clause in the contract, not in a footnote in the sales deck. I got betrayed twice before by fake promises.
I evaluated Toptal and one of the big offshore shops. Trango was the only one that mentioned a 7-axis eval gate to the kickoff and walked me through what actually failed. They were really transparent, and they even told me that six of their last 12 models failed. Nobody else tells you that.
They offered a 90-day drift SLA, which made the procurement comfortable. Every other vendor just delivered and vanished. Trango wrote a retrain-at-our-cost clause into month four of the SOW. That was the moment my CFO stopped pushing back.
I shortlisted three vendors. Of the 2, 1 was not transparent at all and said to contact sales for pricing. On the other hand, Trango had a published rate matrix on their page; that was the point of difference which made them stand out from the rest. That matrix helped me budget without three discovery calls.
The interview was with the actual engineer who’d be in our repo. Unlike others who fix interviews with a sales engineer or a profile “A”. He shared his GitHub profile during the interview. After that, the trial week was just a formality.
We nearly selected a general–purpose AI vendor. The Trango call was the first time anyone said, “You don’t need LLM people for this. They told us that you need people with XGBoost expertise who can write a model card. That honestly mattered more than the pitch.
References available under NDA; we’ll connect you to the speakers above on the scoping call.
A consultant should be honest and truthful, not to win every engagement. If your situation matches one of these, we’ll say it on the first call and give you the right recommendation.
If your project is an exploratory experiment with an uncertain payoff and no plans for launch within a year, the standard engineering evaluation process will reject the wrong candidates. To succeed, hire a PhD-level researcher or collaborate with a university lab.
This page is dedicated to our engineers who ship traditional ML models specializing in tabular, time series, CV, recommendation, and MLOps. If your work is RAG, agentic AI, prompt engineering, LLM fine-tuning, or chatbot development, our hire AI developers page has the right engineers and the right vetting.
If you want a script to run just once, a sandbox proof-of-concept, or a Jupyter notebook for a demo, hire a freelancer on Upwork for $40-$80/hour. Don’t engage a team with an Eval gate and a 5-day replacement SLA. It can cause frustration and lead to over-delivering for the job.
If your work requires experimental design, A/B testing, hypothesis evaluation, statistical inference, or dashboarding, you need a senior data scientist rather than a machine learning engineer. Because these tasks focus on understanding human behavior, interpreting data, and driving business strategy rather than building software. We do have a few data scientists, but it is not our primary specialty.
Sourcing: Our active pool has 240 senior machine learning engineers across the US, Latam, CEE, and India. 60% come from referrals from engineers already in the pool. The rest come from direct inbound and targeted recruiting through our Bengaluru and Houston offices. We do not use Upwork or LinkedIn Lite outreach.
Vetting: Every engineer passes the published 6-stage take-home assessment and a 90-minute structured technical interview with one of our leads. The passing ratio is 14% of qualified applicants. We assess every engineer annually because production ML practice moves.
Rate Matrix: Our rate matrix is refreshed quarterly against secondary sources (secondtalent.com, goLance, Signify Technology benchmarks), and our own internal placement data. Last refresh: . Next refresh: Q3 2026.
Editorial Review: This page is reviewed before publication and every quarter thereafter by Maya Okonkwo (Principal AI Architect) and Marcus Bell (Principal, Talent & Strategy). Factual corrections via [email protected]; we publish corrections with dates.
We have eleven more under NDA. Reach out to us and get the deck; our full list spans healthcare, financial services, retail, manufacturing, insurance, and SaaS.
B2B SaaS · Churn
We developed a machine learning model to predict which mid-market business clients are likely to cancel their SaaS subscriptions. By analyzing billing records, the system flags at-risk accounts with the exact reason for flagging. This accurate, automated tool reduced customer cancellations by 38% and maintained this success for 18 months without requiring manual updates.
Manufacturing · Predictive Maintenance
Our engineers built a system that acts as a 24/7 check engine light for factory machines. By analyzing machine vibrations and data, the AI successfully predicts breakdowns before they happen. This cut unexpected factory shutdowns by 37% over a year and a half, saving time and money.
Insurance · Claims-Loss
An accelerated 12-week technology project where data engineers, senior machine learning engineers, and MLOPs from US/CEE blended. We used an XGBoost ensemble with calibrated Brier scores. They finalized and shipped the required compliance documentation, the SR 11-7 binder. It resulted in 42% faster claims review with no degradation in fraud catch rate.
We have more case studies under NDA; ask for the deck.
If you don’t find your question here, ask us on the scoping call. We’ll either answer it or add it to the page.
A data scientist focuses on analyzing data and building models to generate business insights and prediction outcomes. Machine learning engineers take those models and ship and operate them, including deployment, monitoring, drift detection, and retraining. They are adjacent skill sets, but the wrong hire can waste 6-12 months. See our role decoder for the full breakdown across 4 roles.
Our published blended rates for pods: $48-$72/hr in India. $72–$105/hr (LATAM) · $88–$118/hr (CEE) · $135–$165/hr (US) for senior MLEs. MLOps engineers run 5–10% higher. Research scientists are 25–40% higher. See the full matrix · 4 roles × 4 regions. In-house TCO: $260K–$310K loaded for a US senior in 2026.
Typically, it will take 72 hours for the first qualified candidate to match the first qualified candidate from our pre-vetted pool. We offer a one-week paid trial, with production deployment by day 30 in most cases. We’re flexible, so if you need next-business-day placement for a production fire, we usually do that too; let us know when you book the scoping call.
We provide a mutual NDA before the first technical call. Our engineers also sign individual NDAs before data access. Your data is kept strictly private, legally protected, and handled only by cleared professionals. The system adheres to strict privacy and security laws, including healthcare’s HIPAA, general privacy’s GDPR, and broad business security’s SOC 2. For highly regulated industries, your account and data are managed by US-based engineers who have cleared specific background and compliance checks.
Our contract is incredibly flexible, low risk, and entirely on your terms. You can cancel at any time with just 30 days' notice. Once the notice is given, the vendor immediately cuts off their team’s access to your systems within four hours to protect your security. Within two weeks of leaving, the vendor is legally required to permanently wipe your data from your systems and provide proof that it has been removed.
A working vocabulary for the technical language above. Plain definitions, not marketing copy.
Most clients begin with the scoping call; some run the sizing quiz first, while a few request a shortlist of named engineers before any conversation. Pick the door that fits.
A lead engineer conducts a structured walkthrough of your case, target seniority, region, and timeline. At the end of the call, you’ll have a written role recommendation, rate band, and a suggested team size. We’ll not give you any pitch deck, and there’ll be no BDR cadence after.
5 questions: use case, timeline, data maturity, in-house team, production scope. In return, you’ll get a recommended team composition and monthly cost, a day 30 deliverable, and the best-fit region.
Run Sizing QuizTell us your role, region, and use case, and we’ll send you three named candidates from our pool within 24 hours. Now, you can hire machine learning engineers according to your choice.
@misc{trango_hire_mle_2026,
author = {Okonkwo, M. and Bell, M.},
title = {Hire Machine Learning Engineers},
year = {2026},
url = {https://trangotech.ai/hire-machine-learning-engineers/}
}