Senior Ai Ml Engineer
Core
Design and develop production-ready AI, ML, and Generative AI solutions, converting prototypes into scalable systems.
Role type
Senior AI/ML Engineer
Builds
Production AI systems, RAG pipelines, AI assistants, and inference pipelines
Domain
Generative AI, LLMs, RAG, Computer Vision, Enterprise AI
Deliverable
production ML models
Required skills
Python, Generative AI, LLM applications, RAG, Prompt engineering, Agentic workflows, ML fundamentals, NLP, Computer vision, Model evaluation, Feature engineering, Docker, CI/CD, Cloud platforms (AWS/Azure/GCP), ML libraries (pandas, NumPy, scikit-learn, TensorFlow, PyTorch)
Preferred skills
AWS SageMaker, SageMaker Pipelines, ECR, Step Functions, Snowflake, Vertex AI Vector Search, Model deployment and monitoring, Healthcare domain, Workflow automation, Document intelligence
Technologies
LangGraph, CrewAI, Agno, Haystack, LangChain, LlamaIndex, OpenAI, AWS Bedrock, Vertex AI, Snowflake, React, TypeScript, Node.js, Java, AWS SageMaker, Docker, Git, REST APIs
Responsibilities
Design and develop AI-enabled features using LLMs, GenAI, RAG, and agentic AI patterns; Build RAG pipelines using documents, manuals, business process data, and application data; Implement AI assistants using frameworks such as LangGraph, CrewAI, Agno, Haystack, LangChain, or LlamaIndex; Integrate AI/LLM APIs such as OpenAI, AWS Bedrock, and Vertex AI; Productionize ML models into modular, tested, production-ready Python code; Build batch and real-time inference pipelines using AWS SageMaker, Step Functions, Docker, and cloud services; Monitor model performance, cost, latency, quality, and drift; Apply responsible AI guardrails, output validation, and security best practices
Seniority
Senior, hands-on IC
