- Bachelor's degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent professional experience
- 6+ years of proven software engineering experience with significant hands-on AI/ML work in enterprise environments
- Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders
- Strong knowledge of Agile methodologies and principles
- Demonstrated passion for staying current with the rapidly evolving AI landscape
LangGraph,CrewAI,AutoGen,Python
Agentic AI Architecture & Development
- Design and build production-grade multi-agent systems using LangGraph as the primary orchestration framework, with knowledge of LangChain, CrewAI, and AutoGen
- Architect agent orchestration patterns including planning, tool use, persistent state management, memory, reflection, and multi-agent coordination
- Develop and optimize RAG (Retrieval-Augmented Generation) pipelines with document processing, chunking strategies, embedding workflows, and vector database integration
- Build robust agent evaluation, testing, and observability frameworks to ensure reliability and performance in production
- Design natural language to data query solutions integrating with platforms such as Databricks Genie
LLM Integration & Optimization
- Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic, open-source models) with appropriate model selection, prompt engineering, and cost optimization
- Design prompt engineering strategies including chain-of-thought, few-shot, and structured output techniques for reliable agent behavior
- 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) that serve AI agent workflows to production applications at enterprise scale
- Design and implement 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 that connect AI agents with enterprise data sources, tools, and external services
- Implement distributed task processing (Celery) and event-driven autoscaling (KEDA) for production AI workloads
Innovation & Technical Leadership
- Stay current with the rapidly evolving Agentic AI landscape and evaluate emerging frameworks, models, and techniques
- Lead proof-of-concept development for new AI capabilities, moving 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 the adoption of AI-powered development tools (Cursor AI, GitHub Copilot) across engineering teams
