Agentic Orchestration: Design and develop multi-agent systems and thinking frameworks. Build workflows where AI agents can collaborate and solve dynamic problems end-to-end
System Integration: Wrap business logic, databases, and microservices into structured, callable Tools for Large Language Models (LLMs) to use automatically.
Framework Development: Build and scale systems using AI orchestration libraries and frameworks (e.g., LangChain4j, LangGraph, or AutoGen).
Guardrails & Safety: Establish strict enterprise guardrails encompassing prompt injection defenses, data privacy filters, and Human-in-the-Loop (HITL) approval flows.
Evaluation & Observability: Implement evaluation pipelines and continuous monitoring for LLM latency, hallucination prevention, and contextual accuracy.
Java Mastery: Minimum 2 10+ years (depending on mid/lead level) of experience with enterprise Java, Spring Boot, microservices architecture, and cloud-native development.
AI Knowledge: Applied experience with Agentic AI concepts, prompt engineering, and memory management in LLMs.
Ecosystem Familiarity: Hands-on experience with LLM orchestration (like LangChain4j) and Vector Databases (e.g., Pinecone, Milvus, Qdrant).
Backend Fundamentals: Strong understanding of RESTful APIs, distributed systems, and CI/CD pipelines in major cloud environments (AWS, Azure, or GCP)