Agentic AI Engineer

Role Overview :

We are looking for an innovative Agentic AI Engineer / Developer who will play a key role in designing, developing, and deploying intelligent autonomous systems powered by Agentic AI. You’ll be working at the forefront of AI, where you will architect and implement intelligent agents that can autonomously perform complex tasks, make decisions, and continuously learn. This is an exciting opportunity to work on cutting-edge Agentic AI frameworks, LLM orchestration, and advanced AI workflows that power next-gen autonomous systems.

In this role, you will be responsible for fine-tuning and deploying Large Language Models (LLMs), integrating them into Agentic AI frameworks to create adaptive, intelligent agents. The position requires a deep understanding of Agentic AI protocols (like MCP, A2A), Agentic workflows, and advanced concepts in model inference, to create AI solutions that can autonomously reason, learn, and act.

Key Responsibilities :

• Build Autonomous Agents : Design, develop, and deploy Agentic AI agents using frameworks like LangGraph, AutoGen, Crew AI, and other related platforms. These agents should operate autonomously, making decisions based on complex inputs and evolving situations.
• Agentic AI Workflow Integration : Develop and implement Agentic AI workflows that leverage LLMs, external tools, and memory systems to support modular agent systems that autonomously interact and adapt in real-time.
• LLM Optimization & Fine-Tuning : Fine-tune Large Language Models (LLMs) to fit specific Agentic use cases, utilizing advanced techniques like LoRA, QLoRA, PEFT, and continuous model retraining to improve performance.
• Autonomous System Design : Create intelligent agents that can interact with each other and external systems in real-time, handle complex decision-making processes, and adapt based on the context and feedback loops in dynamic environments.
• Agent-to-Agent Protocols (A2A) : Implement and optimize Agent-to-Agent (A2A) communication protocols, ensuring that multiple agents can collaborate, exchange information, and autonomously solve problems.
• Memory & Knowledge Management : Develop systems for memory management and contextual awareness within autonomous agents, ensuring they retain and utilize prior knowledge to enhance their capabilities. Integrate Knowledge Graphs and Retrieval-Augmented Generation (RAG) techniques for improved reasoning.
• Scalable Inference Management : Optimize the inference of LLMs, ensuring the model can scale effectively across distributed systems, using caching techniques, vector databases (e.g., FAISS, OpenSearch, ChromDB, Pinecone, Milvus), and high-performance computing environments.
• Customer & Stakeholder Interaction : Lead rapid POCs, demos, and custom implementations for customers, ensuring that the technical solutions align with business needs and deliver real-world impact.
• Continuous Innovation : Stay up-to-date with the latest advancements in Agentic AI, LLMs, and autonomous systems to push the boundaries of what intelligent agents can do. Experiment with emerging technologies, algorithms, and frameworks to improve our AI solutions.

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Why Join Us?