[Hiring] Agentic AI Developer @VSI Technologies

Role Description

You build autonomous AI agents and multi-agent systems that execute complex workflows without human intervention between steps. Not chatbots. Not single-turn LLM wrappers. Agents — systems that plan, reason, take actions, use tools, handle failures, and complete multi-step tasks in production environments that enterprise clients and government agencies depend on. You understand that an agent that works in a demo breaks differently than one that breaks in production at 2am. You build for the failure case first. You define what the agent does when a tool call fails, when the LLM returns an unexpected output, when a dependency is unavailable. You do not leave those decisions to the runtime.

What You Do Every Single Day

• Read every written specification before building — every agent has a documented scope, a defined tool set, a failure handling protocol, and acceptance criteria before you write the first line of code.
• Build multi-agent systems using LangChain, AutoGen, CrewAI, or equivalent — you have used at least one in a production deployment and you can explain what broke and how you fixed it.
• Design and implement agent tool integrations with error handling, retry logic, and logging on every single integration without exception.
• Build agent orchestration layers — task decomposition, agent routing, inter-agent communication, state management, and output validation.
• Implement agent memory systems — short-term context management, long-term vector storage, episodic memory for multi-session workflows — optimized for token cost and retrieval performance.
• Build human-in-the-loop checkpoints where specifications require them — government and enterprise deployments require human approval gates before consequential actions.
• Implement full observability on every agent — every reasoning step logged, every tool call recorded, every decision traceable. An agent without an audit trail does not deploy to a government environment.
• Write tests for agent behavior — deterministic tests where possible, evaluation harness testing where outputs are non-deterministic. Test failure paths explicitly.
• Document every agent — architecture, tool inventory, failure modes, escalation logic, and deployment requirements.
• Submit a written daily standup update every working day without exception.

Technical Requirements

• Minimum 3 years of professional experience building AI agents using LangChain, AutoGen, CrewAI, or equivalent — of which at least 2 years must be in production deployments serving real users with real uptime requirements.
• Minimum 4 years of professional experience integrating AI systems into production — including at least 2 years of direct LLM API integration.
• Minimum 5 years of professional production Python — your primary language. Typed, tested, documented, async where appropriate, structured for long-term maintainability.
• Minimum 3 years of implementing observability systems for AI or software in production — of which at least 1 year must be specific to agentic AI observability.
• Minimum 2 years of implementing security controls in AI systems deployed to enterprise or government clients.
• Minimum 2 years of designing and deploying multi-agent systems in production.
• Minimum 2 years of production experience implementing agent memory systems.

What We Require

• 4–6 years of AI and software development experience with at least 2 years specifically in agentic AI systems deployed to production.
• You have built and deployed at least one multi-step AI agent serving real users with real uptime requirements — you can describe how it handles every failure mode.
• You have implemented agent observability — every reasoning step and tool call is logged and traceable in your production systems.