Job Description
Are you ready to architect the autonomous workforce of tomorrow?
Nexus Dynamics is at the forefront of the Agentic AI revolution. We are seeking a visionary Senior AI Agent Engineer to lead the development of autonomous agents that redefine human-machine interaction. As we look toward the future of 2026, we need a technical leader who understands the nuances of Large Language Models (LLMs), tool use, and multi-agent orchestration.
In this role, you will not just write code; you will build intelligent systems capable of reasoning, planning, and executing complex tasks independently.
Responsibilities
- Architect Autonomous Agents: Design and implement sophisticated AI agents capable of complex reasoning, planning, and tool execution using LLMs and frameworks like LangChain or AutoGen.
- Multi-Agent Orchestration: Build scalable systems where multiple agents collaborate to solve enterprise-level problems, managing communication and task delegation.
- RAG & Context Management: Engineer robust Retrieval-Augmented Generation pipelines to ensure agents have access to accurate, real-time data.
- Performance Optimization: Continuously refine prompt engineering strategies and agent architectures to maximize accuracy, reduce hallucinations, and improve response latency.
- Tool Integration: Seamlessly integrate external APIs, databases, and software tools into the agent's decision-making loop.
- Research & Prototyping: Stay ahead of the curve by researching emerging AI paradigms and prototyping innovative solutions for the 2026 landscape.
Qualifications
- 5+ Years of Experience: Strong background in software engineering, specifically with Python or TypeScript.
- LLM Expertise: Deep experience building applications with LLMs, including OpenAI API, Anthropic, or open-source models (Llama, Mistral).
- Agentic Frameworks: Proficiency in building agents using frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGPT.
- MLOps & Deployment: Experience deploying models to production environments (AWS, GCP, or Azure) and managing the lifecycle of AI applications.
- Problem Solving: Ability to debug complex reasoning loops and handle edge cases in AI decision-making processes.
- Education: BS, MS, or PhD in Computer Science, Artificial Intelligence, or a related technical field.