Senior Machine Learning Engineer - LLM/OpenAI

Description :

About the Role

We are looking for a Senior Machine Learning Engineer with 5 -6 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.

Key Responsibilities

Machine Learning & 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.

- Improve model performance via finetuning, quantization, pruning, or distillation when needed.

- Build, train, finetune, and optimize 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 optimize API performance in production.

Infrastructure & DevOps :

- Build and deploy containerized 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 & 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.

Required Skills & Experience :

- 5 - 6 years of hands-on experience as an ML Engineer or similar role.

- Expert-level Python programming and clean code practices.

- Strong experience designing and integrating production APIs.

- Practical experience integrating LLM models and writing optimized prompts.

- Strong understanding of model finetuning, hyperparameter tuning, and inference optimization.

- Experience with Docker, containerized 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).