Agentic AI Implementation Engineer

We're looking for a highly skilled and visionary Agentic AI Implementation Engineer who can design, build, and deploy intelligent systems using Agentic RAG (Retrieval Augmented Generation) techniques and custom GPTs . This pivotal role will involve creating autonomous or semi-autonomous agents powered by Large Language Models (LLMs) that are capable of sophisticated task planning, contextual decision-making, and iterative information retrieval. You will operate at the intersection of advanced prompt engineering, tool orchestration, and multi-step reasoning , with a strong focus on performance, scalability, and user-centric design. Key Responsibilities Architect & Implement Agentic Workflows: Design and implement robust agentic workflows leveraging RAG pipelines, LLM agents, and external tool integrations . Design Modular Systems: Create modular, agentic systems that seamlessly incorporate planning, memory, tool use, and context-aware reasoning capabilities. Develop & Optimize Custom GPTs: Develop and fine-tune custom GPTs utilizing advanced prompt engineering and OpenAI's custom instructions, functions, and APIs . Integrate Knowledge Bases: Integrate diverse knowledge bases, vector stores (e.g., FAISS, Pinecone, Weaviate, Cosmos DB, ChromaDB) , and APIs into a cohesive Agentic RAG architecture. Fine-tune Agent Behaviors: Fine-tune agent behaviors for a variety of real-world applications, such as customer support, research assistants, and code agents. Collaborate Cross-functionally: Work closely with product managers, UX designers, and backend engineers to ship scalable and robust AI solutions. Rapid Prototyping & Experimentation: Rapidly prototype ideas, conduct LLM experiments, and iterate on designs using both quantitative and qualitative metrics. Monitor & Optimize Performance: Continuously monitor system performance, detect and address reasoning failures, hallucinations, and retrieval mismatches to ensure high-quality outputs. Stay Updated: Remain current with the latest research and advancements in Agentic AI, RAG, tool use, and autonomous agents. Must-Have Skills Experience: Proven experience in software engineering, ML systems, or applied NLP/LLM development. Agentic RAG Frameworks: Strong expertise in Agentic RAG frameworks such as LangGraph, AutoGPT, CrewAI, and LangChain Agents . Custom GPTs & OpenAI API: Demonstrated ability to design and implement custom GPTs using advanced prompt strategies and the OpenAI API (functions, tools, memory) . Vector Databases & Embeddings: Hands-on experience with vector databases (e.g., Cosmos DB, Pinecone, ChromaDB), embeddings, and semantic search . Retrieval Augmentation: Deep understanding of retrieval augmentation, context compression, multi-hop querying, and memory management . Programming & LLM Tooling: Fluency in Python and experience with modern LLM tooling (e.g., LangChain, LlamaIndex ). Systems Thinking: Strong systems thinking and the ability to effectively balance trade-offs between model performance, latency, and accuracy. Agile Environment: Comfortable with fast-paced, iterative environments and exploratory development. Desired Skills Autonomous Agents: Experience with autonomous agents and frameworks like AutoGen, OpenAgents, or BabyAGI . AI Safety & Ethics: Understanding of AI safety, ethics, and control mechanisms in agentic systems. LLM Evaluation: Familiarity with evaluation techniques for LLM pipelines (e.g., hallucination detection, prompt testing frameworks).