Job Description
About the Role:
We are seeking an Agentic AI Architecture & Development with 6+ years of proven software engineering experience with significant hands-on AI/ML work in enterprise environments.
Requirements
- Strong proficiency in Python with hands-on experience building production AI applications
- Demonstrated experience with LangGraph or similar agentic AI frameworks (LangChain, CrewAI, AutoGen) for production systems
- Hands-on experience with LLM API integration (OpenAI, Azure OpenAI, Anthropic) and prompt engineering
- Experience designing and implementing RAG systems including embedding models, vector databases, and retrieval strategies
- Solid understanding of multi-agent system design, agent orchestration, persistent state management, and memory patterns
- Experience with Python web frameworks (FastAPI) and distributed task processing (Celery) for production APIs
- Experience with event-driven microservices and real-time streaming patterns
- Proficiency with AI-powered development tools (Cursor AI, GitHub Copilot, or similar) for AI-augmented software development across the SDLC
- Proficiency with Git, CI/CD pipelines, and cloud platforms (preferably Azure)
Preferred Qualifications
- Experience with vector databases (Qdrant, Pinecone, Weaviate, ChromaDB)
- Expertise with Databricks Genie or similar natural language to data query platforms
- Experience with AWS Bedrock AgentCore for managed agent runtime and multi-cloud agent deployment
- Knowledge of model fine-tuning, quantization, and serving optimization
- Experience with multi-tenant architecture patterns and enterprise-scale AI systems
- Familarity with containerization (Docker, Kubernetes) and event-driven autoscaling (KEDA)
- Understanding of AI safety, responsible AI principles, and enterprise governance requirements
Technical Skills
- Primary Framework: LangGraph (multi-agent orchestration with persistent state)
- Additional Frameworks: LangChain, CrewAI, AutoGen
- LLM Providers: OpenAI (GPT-5.X), Azure OpenAI, Anthropic (Claude), enterprise LLM services
- Techniques: RAG, prompt engineering, chain-of-thought, function calling, structured outputs
- Data Intelligence: Databricks Genie (natural language to SQL)
- Vector Databases: Qdrant, Pinecone, Weaviate, ChromaDB
- Multi-Cloud: AWS Bedrock AgentCore (managed agent runtime)
- Patterns: Multi-agent orchestration, tool use, persistent state, memory management, agent evaluation
- Bachelor's degree in computer science, Engineering, AI/ML, or a related technical field, or equivalent professional experience
- 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
