Senior Machine Learning Engineer

## 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).