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).