Hire Machine Learning Engineers Who Ship Models to Production, Not Notebooks
Most ML pilots die between the demo and the deploy. Hire a machine learning engineer from Entrans and you get a senior practitioner who owns the whole path: data pipelines, training, serving, and the monitoring that keeps accuracy from sliding. First profiles land in 24 to 48 hours, and your pick is onboarded within 48 to 72 hours.
Why Market Leaders Choose Entrans Machine Learning Engineers
You are not buying a model demo. You are buying a system that holds up under real traffic, messy data, and an audit. That gap is what our engineers are hired to close.
1. Engineers Who Have Deployed, Not Just Trained
Every machine learning engineer we place has taken a model through deployment, monitoring, and at least one retraining cycle. Candidates who stop at the notebook do not reach your shortlist.
2. One Team for the Whole ML Lifecycle
Data ingestion, feature pipelines, training, serving, and drift monitoring sit with the same people. Nothing falls through the handoff between your data group and your product team.
3. Comfort With Regulated, Messy Enterprise Data
Our engineers work with claims files, invoices, ledgers, and clinical records. They design around PII rules, audit trails, and approval steps early, instead of discovering them a week before launch.
4. GenAI Depth on Top of Classical ML
Forecasting and classification are the baseline. The same team builds retrieval pipelines, fine tuned models, and agent workflows, backed by our agentic AI framework integration practice.
5. Hiring That Fits Your Quarter
Take one machine learning expert or a five person pod. Profiles arrive in 24 to 48 hours, onboarding lands in 48 to 72, and you can scale the team down when the project ends.
Hire Machine Learning Developers From Entrans That Are Certified and Experienced
Entrans is ISO certified and a NASSCOM member, with 150+ AI projects delivered and 500+ domain trained professionals behind the bench. Here is the work a machine learning developer picks up once they join your team.
Data Pipelines and Feature Engineering
They build ingestion and transformation jobs in Spark, Airflow, and dbt, then turn them into versioned features the whole team can reuse. Weak inputs kill more ML projects than weak algorithms, so this comes first. When the platform itself needs work, our data engineering and advanced analytics team steps in.
Model Development and Training
Classification, regression, forecasting, ranking, recommendation, and computer vision, built in PyTorch, TensorFlow, scikit-learn, and XGBoost. They start with the simplest model that clears your accuracy target, then earn any complexity they add.
LLM, RAG, and Fine Tuning Work
Retrieval pipelines on pgvector or Pinecone, embedding and chunking choices, prompt and context design, LoRA fine tuning, and evaluation harnesses that score answers before your users do.
Deployment and Model Serving
Models get packaged in Docker and served through FastAPI, Triton, Amazon SageMaker, Vertex AI, or Azure ML. Latency budgets and cost per prediction are agreed before launch, not discovered on the cloud invoice.
MLOps, Monitoring, and Retraining
CI/CD for models, experiment tracking in MLflow, drift detection, scheduled retraining, and a rollback path that works. Our DataOps and MLOps services team supports the platform side when you need it.
Evaluation, Testing, and Cost Control
Offline metrics, shadow runs, A/B tests, and bias checks before release. After release, they cut GPU spend with quantization, batching, and caching, and they report what each point of accuracy costs.
Schedule Interviews With Machine Learning Experts and Onboard Them Within 48 to 72 Hours
Machine Learning Development Technology Expertise
Languages and ML Frameworks
Data Engineering and Feature Pipelines
GenAI and LLM Engineering
MLOps, Serving, and Cloud
Our Customer Success Stories
Prior Authorization Automation for a Healthcare Payer Platform
Industry: Healthcare
Technical Stack: OCR, large language models, configurable rule engine, microservices, automated testing framework
Patient, eligibility, and provider details arrived incomplete and inconsistent, so every authorization needed manual validation and care approvals crawled. Entrans built OCR and LLM extraction for the unstructured documents, added a configurable rule engine that checks each request against medical and payer criteria, and moved the platform from a monolith to microservices with automated tests and generated validation reports. Processing now runs 3X faster, manual effort and errors fell by 70 percent, and extraction accuracy holds above 95 percent as document volume grows. The full prior authorization automation case study has the detail.
AI Invoice and GRN Reconciliation for a Procurement Enterprise
Industry: Finance and procurement
Technical Stack: AWS Bedrock LLM functions, Flask, MongoDB, PostgreSQL, automated document matching and discrepancy detection
Invoices lived across PDFs, scanned copies, and structured records, and rule based matching could not cope with the format spread. Entrans used LLM functions on AWS Bedrock to pull fields from any format, matched them against purchase orders and goods receipt notes, and flagged quantity, pricing, and supplier gaps without a human in the loop. Manual reconciliation effort dropped 80 percent and payment processing runs 2X faster. See the invoice and GRN reconciliation platform.
Designed for Enterprise Speed and Control
Hiring an ML engineer through job boards takes two to four months, and most of that time goes to screening people who cannot deploy. This route takes days, and every call stays yours.
1. Share Your Requirements
A 30 minute call covers the use case, the data you hold, your cloud, and the accuracy or latency targets that actually matter to the business.
2. Get Curated Profiles (Within 24 to 48 Hours)
Three to five machine learning engineers matched to your domain and stack. You get what they shipped and how it performed, not a keyword resume.
3. Evaluate and Interview
Run your own technical round on your own bar. One question separates the field fast: ask each candidate to walk through a model they put into production and what broke after launch.
4. Onboard and Kickoff (Within 48 to 72 Hours)
We handle contracts, NDA, and access. Your engineer joins standups, reads the codebase, and picks up a first ticket in the same week.
5. Continuous Support and Scaling
Add a data engineer or an MLOps specialist as scope grows, or step down to part time after launch. A delivery manager tracks velocity, code quality, and model performance with you.
Our Hiring Models
Dedicated Machine Learning Developers
Hire dedicated machine learning developers who work only on your roadmap, full time and long term. This fits when ML sits inside the product and someone has to own models after launch, not just at launch.
Team Augmentation
Hire ML engineers, or a mix of AI and ML developers, to extend the team you already have. They join your sprints, your tools, and your code reviews, and you keep planning and priorities. Remote machine learning developers work to an agreed daily overlap with your hours.
Project-Based Engagement
A scoped build with a fixed outcome, such as a first RAG pipeline, a demand forecast, or a move from notebooks to a served endpoint. Many teams hire machine learning consultants this way to prove value before they commit to a permanent seat, then widen the scope through our artificial intelligence services.
Industries Where Our Machine Learning Engineers Deliver Impact
Our team serves global clients across healthcare, banking and financial services, manufacturing, retail, logistics, and real estate. These engineers have built claims automation, demand forecasts, fraud checks, and document extraction inside environments where audits, uptime, and data rules come first.
Chief Executive Officer
Chief Executive Officer
Looking to Hire Machine Learning Engineers Who Can Take Models From Pilot to Production?
Book a Free ConsultationFrequently Asked Questions
What does a machine learning engineer do?
A machine learning engineer builds and runs the systems that put models into production. The work spans data pipelines, feature engineering, model training and evaluation, deployment, and monitoring for drift once the model is live. Most of the role is software and data engineering, not research.
What is the difference between a machine learning engineer and a data scientist?
A data scientist proves that a model can answer the question. A machine learning engineer makes that model run reliably at scale, with pipelines, serving infrastructure, tests, and monitoring. If your model already works in a notebook but stalls before launch, an ML engineer is the hire you need.
How much does it cost to hire machine learning engineers?
Cost depends on seniority, location, and engagement type. Full time salaries in the United States commonly run from about $115,000 to over $220,000, and contract rates in the US market often sit between $50 and $200 an hour. Hiring dedicated machine learning developers through Entrans costs less than a US full time hire, and we share a rate card on the first call.
How quickly can Entrans onboard machine learning engineers?
You receive three to five matched profiles within 24 to 48 hours of the requirements call. After your interviews, onboarding takes 48 to 72 hours, including contracts, NDA, and system access. Most clients move from first call to a working engineer inside a week.
How do you protect our data, models, and intellectual property?
Every engineer works under NDA, and the code, models, and data created for your project stay your property. Entrans is ISO certified, and teams follow your controls for access, environments, and audit logging. For regulated data such as health records or payment information, handling rules are agreed before anyone touches a dataset.
