Overview
AI/ML engineers build and ship machine learning systems that power product features at scale: recommendation engines at Flipkart and Myntra, fraud detection at HDFC and ICICI, voice assistants at Reliance Jio, search ranking at Microsoft and Google. They sit at the intersection of software engineering and applied ML, owning models from data pipelines through training to production serving. Demand has grown 4x since 2022 across Indian product companies and global capability centres.
Day-to-day responsibilities
Design model architectures (transformers, CNNs, gradient boosting); train and fine-tune models on GPU clusters; build feature pipelines (Apache Beam, Spark); deploy models to serving infrastructure (Kubernetes, Triton, BentoML); monitor model drift and accuracy in production; collaborate with data scientists on experiment design; instrument feedback loops for continuous learning; review ML PRs from peers.
Skills required
- Python (PyTorch / TensorFlow)
- Linear algebra and probability
- ML system design
- Distributed training (Horovod, DeepSpeed)
- MLOps tooling (MLflow, Weights & Biases)
- Cloud ML services (SageMaker, Vertex AI)
- Docker and Kubernetes
- SQL and data engineering basics
Education path
Most paths start with B.Tech CSE or MTech CS with ML specialisation. M.Sc Data Science / Applied Statistics also works. Strong PhD pipeline from IISc, IITs, IIITs into research-heavy roles. Self-taught entry is possible at Indian start-ups via Kaggle competitions, Hugging Face contributions, and applied ML portfolios.
Career progression
- 1ML Engineer (0-2 yrs)
- 2Senior ML Engineer (2-5 yrs)
- 3Staff ML Engineer (5-9 yrs)
- 4Principal ML Engineer (9+ yrs)
- 5AI Director / VP
Top employers in India
Useful certifications
- Google Cloud ML Engineer
- AWS Certified Machine Learning
- Deep Learning Specialization (Coursera)
- Hugging Face NLP Course
- fast.ai Practical Deep Learning