What a Machine Learning Engineer does
ML engineers turn models into product features that work reliably in production. In 2026 many Indian teams also expect LLM application work, so the JD should say whether the role is classic ML, LLM-based, or both.
Hiring tip: Separate research from production. If the hire will mostly ship and run models rather than invent them, say so, and drop requirements like papers or a PhD.
Machine Learning Engineer responsibilities
- Build, train and evaluate machine learning models for product features
- Deploy models to production and own their latency, cost and reliability
- Build data and feature pipelines for training and inference
- Monitor models in production for drift and quality, and retrain when needed
- Work with product and data teams to frame problems that ML can actually solve
For senior and lead roles, add:
- Set the ML platform direction: training infrastructure, model serving and evaluation standards
- Lead technical decisions on when to build models in-house versus using LLM APIs
Skills to list
Must-have: Python, Machine Learning, PyTorch, SQL, Docker.
Nice to have: LLM, NLP, TensorFlow, Kubernetes.
Swap in your real stack. Keep must-haves to what the hire will use every week; everything else belongs under nice-to-have.
Experience by seniority
| Title | Typical experience |
|---|---|
| Junior Machine Learning Engineer | 0–2 years |
| Machine Learning Engineer | 2–5 years |
| Senior Machine Learning Engineer | 5–8 years |
| Lead Machine Learning Engineer | 8+ years |