Senior AI Engineer

What you'll do

• Architect and lead the development of multi-agent AI systems using frameworks like LangGraph, CrewAI, and AutoGen.
• Design and operationalize multimodal generative AI pipelines to unify text, image, tabular, and graph data.
• Build production-grade RAG and Graph-RAG systems integrating vector databases and knowledge graphs for semantic retrieval.
• Lead LLM fine-tuning, prompt engineering, and model alignment strategies to adapt foundation models for enterprise use cases.
• Establish robust LLMOps and MLOps pipelines on Databricks using MLflow and other tracking tools.
• Develop high-performance Python backend services for LLM inference orchestration and distributed data workflows.
• Implement Responsible AI practices including bias detection, hallucination mitigation, and safety guardrails.
• Research and productionize advancements in generative modeling, agentic AI, and multimodal transformers.

What we're looking for

• Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering.
• Significant experience leading and delivering complex Gen AI or ML engineering programs in production environments.
• Expert-level experience designing and deploying LLM applications, agentic systems, and RAG pipelines from prototype to production.
• Proficiency with LLM ecosystems including OpenAI, Anthropic, Gemini, Hugging Face, LangChain, and open-source models.
• Strong command of Gen AI engineering patterns such as prompt engineering, chain-of-thought reasoning, and vector embeddings.
• Solid knowledge of ML fundamentals including deep learning with PyTorch or TensorFlow and statistical modeling.
• Advanced Python skills including async programming, FastAPI development, and SQL proficiency.
• Experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and MLOps tools like MLflow.