Key Responsibilities:
Agentic AI Architecture & Development:
- Design and develop production-grade multi-agent systems using LangGraph, with familiarity in LangChain, CrewAI, and AutoGen.
- Architect agent orchestration patterns, including planning, tool usage, persistent state, memory, reflection, and multi-agent coordination.
- Develop and optimize RAG pipelines, including document processing, chunking strategies, embedding workflows, and vector database integration.
- Build robust agent evaluation, testing, and observability frameworks.
- Design natural language to data query solutions integrating with platforms like Databricks Genie.
LLM Integration & Optimization:
- Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic) with model selection, prompt engineering, and cost optimization.
- Implement prompt engineering strategies including chain-of-thought, few-shot, and structured output techniques.
- Implement guardrails, safety mechanisms, and content filtering for AI-generated outputs.
- Evaluate and benchmark models for latency, accuracy, cost, and domain-specific performance.
Platform & Backend Engineering:
- Build scalable Python backend services (FastAPI) to serve AI agent workflows.
- Design caching, rate limiting, persistent agent state, and conversation memory strategies.
- Develop event-driven microservices and real-time streaming for AI agent interactions.
- Develop APIs and integration layers connecting AI agents with enterprise data sources and external services.
- Implement distributed task processing (Celery) and event-driven autoscaling (KEDA).
Innovation & Technical Leadership:
- Stay updated with Agentic AI advancements and evaluate emerging frameworks and techniques.
- Lead proof-of-concept development and transition successful experiments to production.
- Mentor engineers on AI engineering best practices, prompt engineering, and agent design patterns.
- Contribute to technical documentation, architecture decision records, and AI solution design specifications.
- Champion adoption of AI-powered development tools (Cursor AI, GitHub Copilot) across engineering teams.
