Gen AI Developer

Key Responsibilities:
AI Agent Development: Build and orchestrate AI agents using frameworks like LangChain, AutoGen, or CrewAI, implementing self-healing workflows (e.g., Act-Verify-Refine loops).
LLM Integration & Backend: Develop robust backend systems using Python and TypeScript, integrating LLMs into microservices architectures.
Data Management for LLMs: Utilize vector databases (Pinecone, Milvus, Weaviate) for agent memory and architect Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and contextual understanding.
Prompt Engineering: Design and optimize prompt strategies, including automated evaluation frameworks, for high-quality LLM output.
Context Engineering: Manage LLM information ecosystems, including system prompts, RAG implementation, and conversation history.
MLOps & Deployment: Oversee the end-to-end lifecycle of generative models, focusing on inference speed, cost-efficiency, and scalability on cloud platforms (AWS, Google Cloud Platform, Azure).
AI Ethics & Compliance: Ensure adherence to security standards, IP regulations, and safety guidelines for all generative models.
Tool Orchestration: Define and manage the API/tool access for AI agents to optimize accuracy.