Senior AI/ML Engineer

We are looking for a Senior AI/ML Engineer with strong experience in building production-ready AI, ML, Generative AI, and computer vision solutions. The ideal candidate should be able to take prototypes from notebooks or scripts and convert them into scalable, reliable production systems.

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
• Work with vector databases, Snowflake, REST APIs, and full-stack application integrations
• Monitor model performance, cost, latency, quality, and drift
• Apply responsible AI guardrails, output validation, and security best practices
• Support development using Python, React, TypeScript, Node.js, and Java

Requirements
-5+ years of experience in AI/ML, software engineering, or ML systems
-Strong expertise in Python and production-quality ML development
-Hands-on experience with Generative AI, LLM applications, RAG, prompt engineering, and agentic workflows
-Experience with OpenAI, AWS Bedrock, Vertex AI, LangGraph, CrewAI, LangChain, LlamaIndex, or similar tools
-Strong understanding of ML fundamentals, NLP, computer vision, model evaluation, and feature engineering
-Experience with Docker, Git, CI/CD, REST APIs, and at least one cloud platform: AWS, Azure, or GCP
-Experience with ML/data libraries such as pandas, NumPy, scikit-learn, TensorFlow, or PyTorch
-Strong communication, problem-solving, and documentation skills

Nice to Have
Experience with AWS SageMaker, SageMaker Pipelines, ECR, Step Functions, Snowflake, and Vertex AI Vector Search
Experience deploying and monitoring ML models in production environments
Background in healthcare, workflow automation, document intelligence, or enterprise AI applications