Title: Agentic AI Lead
Location: Dallas, TX / San Jose, CA
Employment Type: Contract to hire
Experience: 10 years to 15 years
Role Overview
We are seeking an Agentic AI Lead to design, build, and deploy autonomous AI systems. In this role, you will move beyond basic prompt engineering to create self-reasoning agents that execute complex workflows. You will integrate these agents into our Microservices Architecture and leverage the Model Context Protocol (MCP) to connect LLMs seamlessly with enterprise data sources, development environments, and secure APIs.
Key Responsibilities
l Translate Needs: Convert ambiguous client business problems into highly technical product specifications for engineering activities.
l Manage Expectations: Communicate limitations transparently while proposing viable alternative architectures
l Agent Architecture: Design and deploy multi-agent systems capable of autonomous planning, reasoning, reflection, and task execution
l MCP Tooling Development: Build and maintain Model Context Protocol (MCP) servers and clients to securely expose data, file systems, and enterprise tools to LLMs.
l Microservices Integration: Wrap AI agents, RAG engines, and vector stores into modular, scalable microservices using Docker and Kubernetes.
l Tool & API Orchestration: Implement advanced tool-calling architectures to connect LLMs to production databases, external APIs, and internal software systems
l Pipeline Optimization: Evaluate and optimize agentic workflows for token efficiency, latency, context-window usage, and decision-making accuracy.
Required Skills & Qualifications
l Programming: Expert proficiency in Python or TypeScript.
l AI Orchestration: Hands-on experience with LangGraph, CrewAI, AutoGen, or LangChain.
l Model Context Protocol: Practical experience implementing or consuming open-source and custom MCP tools/servers.
l Architecture: Deep understanding of distributed systems, REST/gRPC APIs, message brokers (Kafka/RabbitMQ), and microservice communication.
l Cloud & DevOps: Strong experience with containerization (Docker) and deploying services to cloud infrastructure (AWS, Google Cloud Platform, or Azure).
l AI Expertise: Experience on vector databases embeddings, semantic search, and RAG pipelines
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