Machine Learning Engineer
Also posted as: ML Engineer · MLOps Engineer · Applied Scientist · Data Scientist (ML)
A Machine Learning Engineer in India builds models and, more often than the title suggests, everything around them: preparing data, training and evaluating models, then packaging them into containers, deploying to AWS, Azure or GCP and watching for drift, latency and cost in production. Hiring splits into two tracks - applied ML at product companies (Amazon, Adobe, Conga, 6sense, Coinbase, Simplismart) and MLOps/platform work at IT-services and enterprise teams (TCS, LTIMindtree, Accenture, Straive, Qualys, SIXT), where 8 of the 31 job postings collected carry 'MLOps' in the title. It is the biggest AI job market in the country - LinkedIn alone lists 9,000+ ML engineer roles in Bengaluru - but not the easiest door: only 4 of 31 job postings accepted 0-2 years, so a fresher needs a deployed, monitored project rather than coursework.
- Across 88 Machine Learning Engineer job postings in India, the most-requested capabilities are Train and evaluate classical ML models (99%), Write production-quality Python for AI work (89%) and Operate an ML pipeline (train → register → serve → monitor) (66%).
- Pay at 2–5 yrs averages about ₹12.7 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor · PayScale disagrees); at 5+ years it averages about ₹21.4 LPA; employers offer ₹20–35 LPA (median of 13 job postings that state pay, 2–5 yrs · Wellfound, LinkedIn, Naukri).
- 24,000+ open roles in India — one portal's count, not cross-checked yet, checked 04-10-2026.
- Hiring is concentrated in Bengaluru, Hyderabad and Delhi NCR.
- Postings read from LinkedIn 50%, company career pages 23%, Wellfound 20% and other portals 7%.
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Capabilities employers ask for
How often employers ask for each capability, measured across the job descriptions behind this page. Click one to see what "knowing it" means, how to learn it, and how to prove it.
Have a posting open? Check it against this map →
Train and evaluate classical ML models
Every posting assumes you can train and judge a model. EarnIn wants "strong depth across core machine learning algorithms", Quantiphi lists logistic regression and cluster analysis, and Optum India wants you to own "data preparation, feature engineering, model development, evaluation" through to deployment. You must be able to pick a model, split the data honestly, and explain why its numbers are real.
Explore 5 practice tools →Write production-quality Python for AI work
Python is the house language. Amazon will take Java or C++ alongside it, but Coinbase wants "production-grade Python services", Atlassian wants "performant production-quality code", and BNP Paribas asks for decorators, code quality and dependency hygiene. You need to write clean, tested Python that someone else can deploy, not just a notebook that ran once.
Explore 4 practice tools →Operate an ML pipeline (train → register → serve → monitor)
The job is the whole loop, not just the training run. eBay wants you to "own the MLOps pipelines" for training, validation and monitoring, Optum India lists CI/CD, monitoring, retraining and version control, and Wayfair and Nielsen name Airflow, Kubeflow and MLflow. You should be able to take a model from training to a registry to serving, and notice when it starts to drift.
Explore 5 practice tools →Deploy an AI service to the cloud
Models are expected to leave the laptop and land on a cloud. Cohere Health and InCommon name AWS SageMaker, Optum India wants Azure ML and Databricks, and IDfy runs "primarily on GCP and AWS". You should be able to put a model behind an endpoint on at least one major cloud and keep it up.
Explore 4 practice tools →Build and train neural networks in PyTorch
Unlike GenAI-wrapper roles, this one still expects you to train neural nets. IDfy wants people who have "trained deep learning models from scratch and fine-tuned pretrained networks, in PyTorch", Cohere Health wants deep learning framework expertise, and Amazon asks for neural deep learning experience. You need to build, train and debug a network in PyTorch yourself.
Explore 3 practice tools →Build scheduled data pipelines
Models are only as fresh as the data feeding them. eBay wants "robust data pipelines" for training and feature engineering, BNP Paribas names ETL and Airflow, and Atlassian and Quantiphi want Spark and PySpark. You need to build a scheduled pipeline that pulls, cleans and validates data before a model ever sees it.
Explore 3 practice tools →Automate builds, tests and deploys with CI/CD
Deploys are supposed to be automatic and boring. Solventum wants CI/CD pipelines "focusing on automated testing, model deployment, and version control", Aptiv asks for GitHub-based pipelines, and Cognizant lists GitHub Actions and AWS CodePipeline. You need to wire a repo so that a merge runs the tests and ships the model without anyone clicking buttons.
Explore 3 practice tools →Containerize an application with Docker
Docker is how models get packaged here. EXL wants you to "containerize machine learning applications and predictive models using Docker", Solventum uses Docker to package ML workloads, and Xipper deploys ML services with Docker. You should be able to write a Dockerfile for a model service that builds the same way everywhere.
Explore 2 practice tools →Run workloads on Kubernetes
Kubernetes shows up wherever models are served at scale. Persistent Systems wants cluster management and scaling, MaxHome.ai wants deployments on EKS, GKE or AKS, and Wayfair and IDfy list it next to Docker and the cloud. You don't need to run a cluster from scratch, but you should be able to deploy, scale and debug a model service on one.
Explore 3 practice tools →Build a multi-step agent workflow
Agents have reached the ML engineer job description. Optum India wants agentic applications built on LangGraph and LangChain, Demandbase wants "tool/function calling, and agentic workflows", and Observe.AI wants recent experience building agentic systems. You should be able to build a multi-step agent that calls tools, and know how to test it when it wanders.
Explore 4 practice tools →Integrate LLM APIs into an application
LLMs have crept into the plain ML engineer job. Optum India wants 'hands-on experience integrating LLM provider APIs (e.g., Anthropic Claude, OpenAI, Azure OpenAI)', Demandbase builds LLM applications with retrieval and tool calling, MaxHome.ai integrates OpenAI and Anthropic into pipelines, and CUBE even hands you the provider relationships and token bills. You should be able to call an LLM from code, ground it with retrieval, and judge whether its output is any good.
Explore 4 practice tools →Optimize inference cost and latency
Fast and cheap inference is a named requirement, not a nice-to-have. Pure Storage wants low-latency serving with NVIDIA Triton, UST asks for vLLM plus ONNX and TensorRT, and Netradyne mentions caching and token cost optimisation. You should know how to measure latency and cost, then cut them with quantization, batching or a smaller model.
Explore 3 practice tools →Fine-tune an open LLM (LoRA/QLoRA)
Some employers want you to adapt models, not just call them. Pragmatike wants "hands-on experience fine-tuning transformer-based models", RoundCircle names LLM fine-tuning pipelines, and MaxHome.ai lists fine-tuning next to quantization and distillation. You should be able to fine-tune an open model on your own data and show it beat the base model.
Explore 3 practice tools →Trace, monitor and debug LLM apps in production
Once a model is live, someone has to watch it. UST and GyanSys want drift detection with Evidently AI plus Prometheus and Grafana, Optum India asks for LLM observability and evaluation tooling, and CUBE lists "LLM observability and provider governance". You should be able to trace requests, track quality over time, and find out why an answer went wrong.
Explore 4 practice tools →Build image models (detection/classification)
Vision is a side door here, not the main hall. Amazon wants computer vision algorithm experience, IDfy accepts depth in detection, classification or anti-spoofing as one of its specialisms, and Celex Technology calls CV, OCR or video analytics an "added advantage". Knowing how to train and evaluate an image classifier or detector widens which ML engineer jobs you can take.
Explore 3 practice tools →Apply responsible-AI and data-protection basics
Governance arrives through regulated employers. Optum India wants fairness, explainability and bias mitigation, InCommon wants familiarity with HIPAA, and talentxo wants compliance "within BFSI regulatory standards". You should know how to document a model, check it for bias, and keep personal data where it belongs.
Explore 3 practice tools →Families: Machine learning & data science · Programming foundations · Cloud, deployment & production · Data engineering & analytics · Agents & workflows · LLM application development · Evaluation, safety & observability · Product, business & communication
"AWS" on a JD is not "learn AWS"
The words employers write, translated into what they want you to be able to do for this role.
Skip, for now
- Training an LLM or writing a transformer from scratch — 0 of 31 job postings ask for it. Seven ask you to fine-tune an existing open model; the rest want you to train ordinary models well and ship them. Read the architecture, don't reimplement it.
- MCP servers and multi-agent frameworks (CrewAI, AutoGen) — MCP, CrewAI and AutoGen appear in 0 of 31 job postings. Agent work itself is real now - 7 postings want it, up from 1 - but every one of them means LangGraph-style orchestration bolted onto a deployed service, so build and deploy the service first.
- Reinforcement learning — Appears in 1 of 31 job postings (TAAS Partners, a 30-40 LPA senior lead role). Interesting, but it will not get you hired at 0-4 years in India.
- Terraform / Ansible and infrastructure-as-code — In 5 of 31 job postings, all senior platform roles (Simplismart, Adobe, Straive, Meril, Tiso Studio). Learn Docker plus one managed cloud service first; IaC is picked up on the job in a fortnight.
- Kaggle medals and research publications — Only the 2 Amazon Applied Scientist job postings reward patents or publications (and they also want a PhD or Master's). The other 29 ask for shipped, monitored production systems - one deployed pipeline beats five notebooks.
Train, ship and keep alive: a drift-monitored ML service
Take one real tabular or image dataset, train a baseline scikit-learn model and a small PyTorch model, and track every run in MLflow so the winning experiment is reproducible. Register the best model, serve it behind a versioned FastAPI endpoint inside a Docker image, and deploy it to a free tier on AWS, Azure or GCP. Wire a GitHub Actions pipeline that runs tests, rebuilds the image and redeploys on merge, and add monitoring that measures p95 latency plus data drift on incoming requests, alerting and retraining when drift crosses a threshold. This is the loop that TCS, LTIMindtree, Qualys, Adobe and Straive describe almost line for line in their JDs.
- MLflow shows 10+ tracked runs and a registered model version; the README names the metric, the winning run and one experiment that failed and why
- Model is served from a Docker image at a public URL with a versioned endpoint; p95 latency and cost per 1,000 predictions are measured and documented
- GitHub Actions runs tests and redeploys on merge, and you demonstrate a rollback to the previous registered model version
- A drift monitor (Evidently or equivalent) on a held-out stream fires an alert and triggers a retrain, with before/after metrics committed to the repo
What the interviews look like
The rounds you'll actually face, in the order they usually come.
- 1
Screening
Recruiter or hiring manager checks years of hands-on production ML, which models you have actually shipped, one cloud (AWS/Azure/GCP), MLOps tooling (MLflow, Kubeflow, Airflow, Docker) and notice period. Experience filters are hard here - TCS states outright that under 4 years will not be considered - so lead with a deployed project link.
- 2
Technical / take-home
Python and ML coding: data structures and algorithms at product firms (2 of 31 job postings, both Amazon, ask for it explicitly), plus a take-home to train and evaluate a model on a supplied dataset or containerise and serve an existing one. Expect follow-ups on feature engineering, class imbalance, overfitting, choice of metric and why a model regressed.
- 3
System/product design
Design an end-to-end ML system: ingestion and feature pipeline, training and experiment tracking, model registry and versioning, serving (batch vs real-time vs edge), monitoring for data and concept drift, retraining triggers, rollback, and GPU cost/latency trade-offs. MLOps interviews go deep on Kubernetes, CI/CD and failure recovery.
- 4
Culture / stakeholder
Working with data scientists, data engineers and product owners - how you productionise someone else's notebook, how you explain a model's limits, and how you handle on-call for a degraded model. Domain-heavy employers probe context: healthcare compliance (Meril: HIPAA/GDPR), security and governance (Qualys, Straive), legal NLP (Conga).
What people ask before choosing this role
Can a fresher get a Machine Learning Engineer job in India?
Yes, this is one of the more reachable AI-era roles. 10 of the 88 job postings behind this page accept 0–2 years of experience. The rest want more, so expect the fresher-friendly openings to be competitive.
What is the salary of a Machine Learning Engineer in India?
Pay at 2–5 yrs averages about ₹12.7 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor · PayScale disagrees); at 5+ years it averages about ₹21.4 LPA; employers offer ₹20–35 LPA (median of 13 job postings that state pay, 2–5 yrs · Wellfound, LinkedIn, Naukri). Not every posting states pay, and pay varies widely by city and by whether the employer is an IT-services firm, a global capability centre or a product startup.
How long does it take to become a Machine Learning Engineer?
The six capabilities employers ask for most add up to roughly 172 focused hours — about 22 weeks at 8 hours a week, if you are starting from zero on all of them. Most people are not: the self-check on this page works out what you can skip, which is usually a large part of it.
What skills do you need for a Machine Learning Engineer role?
Across the 88 job postings behind this page, the most-requested capabilities are Train and evaluate classical ML models (99% of postings), Write production-quality Python for AI work (89% of postings) and Operate an ML pipeline (train → register → serve → monitor) (66% of postings). Note these are capabilities, not tools — employers write tool names, but what they are buying is the ability to do the work.
Which cities in India post the most Machine Learning Engineer jobs?
Bengaluru (49), Hyderabad (10), Delhi NCR (10) and Pune (7) — counted across the 88 job postings behind this page. Remote-India roles are counted separately where the posting said so.
Is demand for Machine Learning Engineer roles in India growing?
Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026 against 14% for white-collar hiring overall, with the steepest rise at the senior end (13-16 years' experience +87%). In the ML Engineer job postings collected here with a posted date since 1 September, 25 of 55 ask for 2-5 years, 22 for 5+ and only 1 is open to 0-2 years; 9 of the latest 25 are MLOps-titled or MLOps-first.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 88 job postings behind it asked for one by name. What they ask for is evidence you can do the work — a public repo, a deployed project, something a hiring manager can open. That is what the path on this page is built to produce.
Companies with this role open in India
A sample of employers we saw hiring for this role — IT services, global capability centres, product companies and startups. Each links to one of the company's postings for this role, checked open on 04-10-2026.
What it pays
most earn ₹5.2–10 LPA (base pay) · verified across 3 salary sites: AmbitionBox, Glassdoor, PayScale
Employers offer ₹4–7 LPA: median of 11 job postings that state pay, 0–2 yrs · Wellfound, other sites, Naukri
verified across 2 salary sites: AmbitionBox, Glassdoor · PayScale disagrees
Employers offer ₹20–35 LPA: median of 13 job postings that state pay, 2–5 yrs · Wellfound, LinkedIn, Naukri
verified across 2 salary sites: Glassdoor, PayScale
Employers offer ₹30–35 LPA: median of 9 job postings that state pay, 5+ yrs · Wellfound, Naukri, company sites
Across all levels: the middle half earns ₹6.9–16.1 LPA · Glassdoor
How much demand
What each job portal shows for this role's title — the readings behind the openings figure above.
- 24,10494 of 100 inspected results carry the title · checked 04-10-2026naukri
- at least 11,00017 of 26 inspected results carry the title · checked 04-10-2026linkedin
- at least 400exact-phrase search; Indeed rounds this headline, so it is a floor · disagrees with the others, not used · checked 04-10-2026indeed
Where this role is heading
- Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026 against 14% for white-collar hiring overall, with the steepest rise at the senior end (13-16 years' experience +87%).
- In the ML Engineer job postings collected here with a posted date since 1 September, 25 of 55 ask for 2-5 years, 22 for 5+ and only 1 is open to 0-2 years; 9 of the latest 25 are MLOps-titled or MLOps-first.
Capability percentages come from 88 job descriptions read in full on 03-10-2026. How we do this