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, Prodigal, Simplismart) and MLOps/platform work at IT-services and enterprise teams (TCS, LTIMindtree, Accenture, Straive, Qualys), where 6 of the 20 job ads 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 2 of 20 job ads accepted 0-2 years, so a fresher needs a deployed, monitored project rather than coursework.
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.
Write production-quality Python for AI work
19 of 20 job ads name Python, and it is never optional - Amazon accepts Java or C++ alongside it, everyone else wants clean, testable, debuggable production Python (NumPy/Pandas, PyTorch, FastAPI).
Train and evaluate classical ML models
15 of 20 job ads ask you to actually train and evaluate models - feature engineering, tuning, validation and metrics. The 5 that don't are pure MLOps platform roles where data scientists hand you the model.
Build and train neural networks in PyTorch
14 of 20 job ads name PyTorch, TensorFlow or Keras directly; unlike GenAI roles, this job still expects you to train neural nets (CNNs at Meril and ParallelDots, transformers at Conga, speech models at ViH Labs).
Deploy an AI service to the cloud
14 of 20 job ads require deploying models to AWS, Azure or GCP (SageMaker, Azure ML, Vertex AI, Databricks) or to edge boxes - Biz-Tech wants NVIDIA Jetson plus cloud. Pick one cloud and go deep before adding a second.
Operate an ML pipeline (train → register → serve → monitor)
13 of 20 job ads describe the full loop - train, track, register, serve, monitor, retrain - and name MLflow, Kubeflow, Airflow or DVC. This is the single biggest difference between an ML engineer JD and a data-scientist JD in India.
Automate builds, tests and deploys with CI/CD
10 of 20 job ads require CI/CD for models, not just code (Jenkins, GitHub Actions, GitLab CI, Azure DevOps); TCS and Qualys go further and ask for continuous training triggered by drift or new data.
Containerize an application with Docker
8 of 20 job ads name Docker outright and 2 more imply it via Helm/EKS, so 10 of 20 expect you to package a model into an image with a serving entrypoint. It is the cheapest skill on this list to prove in a portfolio.
Build scheduled data pipelines
10 of 20 job ads put data engineering in the job: Airflow/Dagster/dbt at The Product Highway, Spark and Kafka at Straive and Conga, AWS Glue/EMR at Accenture. Models are a small part of the code you will write.
Build image models (detection/classification)
8 of 20 job ads involve vision - object detection and OCR at ParallelDots, YOLO on Jetson at Biz-Tech, medical imaging CNNs at Meril, vision-language models at The Product Highway. It is the most common specialism in this dataset.
Integrate LLM APIs into an application
7 of 20 job ads expect an ML engineer to also wire in LLMs - Prodigal wants prompt engineering and API integrations, Qualys names LangChain/LangGraph, Accenture wants LLM pipelines in production. Useful leverage, but it does not replace classical ML here.
Run workloads on Kubernetes
7 of 20 job ads require Kubernetes, and every one of them is an MLOps or platform role (TCS, Adobe, Qualys with Helm, Straive, LTIMindtree, Meril, Accenture with EKS/ECS). It is the fork in the road between the applied-ML and platform tracks.
Optimize inference cost and latency
6 of 20 job ads ask for inference optimisation - Simplismart names TensorRT, Triton and vLLM, Meril wants quantisation and BentoML, Biz-Tech wants latency and throughput tuned on constrained hardware. This is where senior pay separates.
Fine-tune an open LLM (LoRA/QLoRA)
5 of 20 job ads want LLM fine-tuning specifically (Conga: transformer fine-tuning with PEFT/LoRA; ViH Labs: LoRA/QLoRA; TAAS, Qualys, Accenture). Do it only after you can train and evaluate a plain model end to end.
Trace, monitor and debug LLM apps in production
Only 3 of 20 job ads ask for LLM-specific tracing today (Accenture: latency, throughput, prompt performance, hallucinations; Qualys names LangSmith; Simplismart wants observability for inference), but classical model monitoring appears in 8 - expect this to become the standard extension of drift monitoring.
Families: Programming foundations · Machine learning & data science · Cloud, deployment & production · Data engineering & analytics · LLM application development · Evaluation, safety & observability
"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 20 job ads ask for it. Five 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.
- Agent frameworks (LangGraph, CrewAI, AutoGen) — Named in 3 of 20 job ads and only in passing. Agent-building is the GenAI engineer's job description, not this one - come back to it once MLOps and deployment are solid.
- Reinforcement learning — Appears in 1 of 20 job ads (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 4 of 20 job ads, all senior platform roles (Simplismart, Adobe, Straive, Meril). 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 3 Amazon Applied Scientist job ads reward publications (and they also want a PhD). The other 17 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 (3 of 20 job ads, all 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. 2 of the 20 job ads 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?
Entry-level roles cluster around ₹3–9 LPA, rising to about ₹50 LPA with experience. Every band on this page is quoted from a named source with a link, and pay varies widely by city, company type and 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 153 focused hours — about 20 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 20 job ads behind this page, the most-requested capabilities are Write production-quality Python for AI work (95% of ads), Train and evaluate classical ML models (75% of ads) and Build and train neural networks in PyTorch (70% of ads). 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 have the most Machine Learning Engineer openings?
Bengaluru (10), Pune (3), Hyderabad (2) and Mumbai (1) — counted across the 20 job ads behind this page. Remote-India roles are counted separately where the ad said so.
Is demand for Machine Learning Engineer roles in India growing?
AI/ML is the fastest-growing job family Naukri tracks: job ads up 33% YoY in July 2026 and 25% in June, against 5-6% growth for white-collar hiring overall, and +45% across FY26. Growth is sharpest at the senior end (AI/ML roles above INR 30 LPA grew 27% in May 2026), and the skill mix has moved towards operations - MLOps tooling appears in 12 of the 20 job ads collected, LLM/RAG/fine-tuning in 10.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 20 job ads 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.
AmazonWhat it pays
Where this role is heading
- AI/ML is the fastest-growing job family Naukri tracks: job ads up 33% YoY in July 2026 and 25% in June, against 5-6% growth for white-collar hiring overall, and +45% across FY26.
- Growth is sharpest at the senior end (AI/ML roles above INR 30 LPA grew 27% in May 2026), and the skill mix has moved towards operations - MLOps tooling appears in 12 of the 20 job ads collected, LLM/RAG/fine-tuning in 10.
Capability percentages come from 20 job descriptions read in full on 24-08-2026. How we do this