Senior Machine Learning Engineer

Overview

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).