AI Agent Engineer

About the Role We are seeking a skilled AI Agent Engineer with hands-on experience in MCP (Model Context Protocol) server architecture , agentic AI frameworks (such as LangGraph, LangChain, AutoGen), and multi-agent system development . The ideal candidate is passionate about building scalable, reliable, and production-ready AI agent ecosystems that can orchestrate tools, workflows, and reasoning capabilities across complex tasks. You will work closely with our AI platform, product, and engineering teams to design, implement, and optimise intelligent agentic systems that power next-generation automation, analytics, and content generation applications. Key Responsibilities 1. Agentic System Design & Development Design and implement multi-agent architectures for workflow automation, data processing, analytics, and content generation. Build robust agent coordination, messaging, memory, and tool-use pipelines using frameworks such as LangGraph, LangChain, AutoGen, Swarm, or custom orchestration engines . Integrate retrieval-augmented generation (RAG), tool APIs, and domain-specific logic into the agent lifecycle. Implement task delegation, role-based agents, and long-context planning for complex multi-step tasks. 2. MCP Server & Tooling Architecture Develop and maintain MCP server components in alignment with OpenAI's Model Context Protocol. Build custom MCP tools , schema interfaces, and secure protocol endpoints for external services and knowledge sources. Ensure scalability, low-latency communication, and version-safe integration of models and tools. Work with platform engineers to ensure seamless integration with our internal systems and third-party services. 3. System Integration & Infrastructure Integrate multiple AI components (LLMs, vector stores, knowledge bases, search engines, APIs, microservices). Optimise agent execution pipelines for performance, cost efficiency, concurrency, and fault-tolerance. Deploy and monitor agent services on cloud platforms (AWS, GCP, Azure) or containerised environments (Docker, Kubernetes). Implement observability for agent execution flows: logs, traces, datastore tracking, and analytics. 4. AI/LLM Foundation Fine-tune or configure LLMs for specific agent behaviours, tool-use strategies, and domain reasoning. Work with embeddings, RAG, indexing pipelines, memory graphs, and prompt-engineering patterns. Evaluate and benchmark models, agent routing strategies, and reasoning algorithms. 5. Collaboration & Delivery Work closely with product managers, solution architects, and UI/UX teams to translate requirements into technical implementations. Provide documentation, architecture diagrams, API specifications, and operational playbooks. Participate in code reviews, design discussions, and technical decision making. Required Qualifications 3–7+ years of software engineering experience , with focus on AI/LLM systems. Hands-on experience building MCP servers and tools ; deep understanding of Model Context Protocol. Strong proficiency in Python , with production experience in frameworks like FastAPI, Django, or Node.js if relevant. Demonstrated experience with agentic frameworks such as: LangGraph (strongly preferred)LangChain Agents AutoGen Swarm or custom orchestration frameworks Experience developing and scaling multi-agent workflows or autonomous agent systems. Solid understanding of LLMs, embeddings, vector databases ( FAISS, Chroma, Pinecone, Weaviate ), and RAG pipelines. Experience integrating external APIs, knowledge bases, search engines, databases, or internal systems. Familiarity with cloud platforms ( AWS/GCP/Azure ), Kubernetes, Docker, microservice architectures. Strong software engineering fundamentals: architecture, testing, CI/CD, version control. Preferred Qualifications Experience building enterprise-grade agentic solutions with reliability, auditability, and observability. Background in distributed systems , workflow orchestration, or event-driven architectures. Experience with MCP-enabled tools (OpenAI, Anthropic Claude, etc.). Experience in LangGraph's new features: Checkpoints, Branching, Isolated Node Execution, DAG-based agent planning. Knowledge of data engineering, analytics platforms, or ETL pipelines. Experience in multimedia or multimodal agent systems (text-image-video workflows). Soft Skills Strong problem-solving ability with a systems-thinking mindset. Ability to work in fast-moving, ambiguous environments. Good communication and cross-functional collaboration skills. Passion for emerging AI technologies and agent-based architecture design. Why Join Us Build cutting-edge agentic AI that powers next-generation enterprise workflows. Work with a senior engineering and AI research team on innovative multi-agent solutions. Influence architecture decisions and shape an evolving AI platform. Opportunity to work with GPT‐5/Claude/LLM orchestration frameworks and experimental agentic technologies. #J-18808-Ljbffr