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).Skills Required
Implementation Services, Prototyping, Performance Optimization, Api