p>Key Responsibilities: • Lead the end-to-end development of advanced AI solutions, including agentic systems, RAG pipelines, and multi-agent workflows • Design and implement embedding and chunking strategies essential for effective Retrieval-Augmented Generation (RAG) across enterprise data • Create integrations to connect AI agents with enterprise data systems, APIs, and services • Develop robust, production-grade asynchronous Python applications with proper error handling and session management • Implement vector database and graph database solutions for semantic search and knowledge representation • Establish agentic architecture, including data flows, interfaces, and mechanisms across all enterprise systems • Deploy and manage AI agents on cloud platforms using CI/CD pipelines, infrastructure as code, and managed AI services • Implement monitoring, logging, and observability for agent interactions to guarantee system reliability and support debugging • Develop context engineering strategies that assemble dynamic context for consistent and accurate Large Language Model (LLM) behavior • Engage with clients to translate business requirements into AI solution architectures, and balance feature development with technical debt management.
Additional Skills:
• Experience with AI/ML agent orchestration frameworks such as Google ADK, LangChain, or similar tools • Experience with multi-agent architectures and workflow orchestration patterns • Experience with LLM evaluation methodologies, including metrics design, hallucination detection, and A/B testing for prompt optimization • Understanding of context engineering principles, including dynamic context assembly, tool result formatting, and conversation state management • Experience with Snowflake or BigQuery, including query optimization and connection management • Experience managing data systems, including persistence selection, schema design, and read/write tradeoffs • Familiarity with MLOps patterns and model deployment pipelines.