We are looking for a Machine Learning Engineer with 3+ years of industry experience to lead the design, development, and deployment of AI-powered systems. This role combines hands-on ML engineering, backend development, LLM integration, and production-grade infrastructure work. You will work closely with product and engineering teams to build reliable, scalable, and high-impact machine learning features.

The candidate will have responsibilities across the following functions:

Machine Learning and LLMs:

- Integrate LLMs (OpenAI, Anthropic, etc. ) into pipelines, prompting, workflows, RAG, evaluation, and iteration.
- Design robust prompt engineering strategies and maintain prompt libraries across environments.
- Build AI systems for OCR, document understanding, information extraction, and classification.
- Improve model performance via fine-tuning, quantisation, pruning, or distillation when needed.
- Build, train, finetune, and optimise ML and LLM-based models for production use cases.

Backend Engineering:

- Develop scalable backend systems using Python (FastAPI/Flask preferred).
- Architect and integrate REST APIs, rate limiting, and monitoring.
- Debug, profile, and optimise API performance in production.

Infrastructure and DevOps:

- Build and deploy containerised applications using Docker.
- Manage model and service deployments on Kubernetes (EKS, GKE, AKS, or self-managed clusters).




- Work with CI/CD pipelines to ensure smooth releases and automated testing.
- Implement logging, monitoring, and alerting for ML and backend services.

Collaboration and Leadership:

- Work closely with cross-functional teams to convert business problems into ML solutions.
- Provide technical guidance to junior engineers and contribute to architectural decisions.
- Bring a strong bias for shipping, iteration, and maintaining high engineering standards.

Requirements:

- 3 to 6 years of hands-on experience as an ML Engineer or similar role.
- Expert-level Python programming and clean code practices.
- Robust experience designing and integrating production APIs.
- Practical experience integrating LLM models and writing optimised prompts.
- Strong understanding of model fine-tuning, hyperparameter tuning, and inference optimisation.
- Experience with Docker, containerised deployments, and Kubernetes orchestration.
- Good understanding of microservices architecture, distributed systems, and cloud infrastructure.
- Solid problem-solving and debugging skills across the ML lifecycle.

Nice-to-Have:

- Experience with vector databases (Pinecone, Weaviate, FAISS).
- Experience with event-driven architecture (Kafka, Pub/Sub, SQS/SNS).
- Exposure to data pipelines (Airflow, Prefect, Dagster).

📌 Machine Learning Engineer (Bengaluru)
🏢 ProductNOVA
📍 Bengaluru