AI Agent Engineer

Role Description

We are hiring an AI Agent Engineer to be the technical engine behind this work. This is a hands-on, high-autonomy role: you will design, build, and deploy AI agent systems for clients, drive internal agent-powered automation for LaunchPad Lab itself, and help define what great AI agent development looks like across the consultancy.

Your time will be split roughly 50/50 between client-facing agent development and internal AI initiatives. You will report to the CTO.

We’re looking for an agent builder who can sit with a client, identify where autonomous workflows create real leverage, design the agent system, and ship it.

What You Will Do

• Client Delivery (~50%)
  • Design and build production AI agent systems for clients across industries, from scoping through deployment and iteration.
  • Lead technical discovery workshops to identify high-value automation opportunities in client workflows.
  • Select and integrate the right agent frameworks, platforms, and models for each engagement (LangChain, LangGraph, CrewAI, Claude Managed Agents, custom orchestration, and others as the landscape evolves).
  • Design human-in-the-loop workflows, approval gates, and escalation paths so agent systems operate safely in client environments, especially in regulated or risk-sensitive industries.
  • Translate complex AI capabilities into clear, actionable recommendations for non-technical stakeholders.
  • Collaborate with LaunchPad Lab’s engineering and design teams to deliver end-to-end solutions.
• Internal Agent Strategy & Operations (~50%)
  • Own LaunchPad Lab’s internal agent automation roadmap. Identify processes ripe for agent-driven improvement and build the solutions.
  • Evaluate new agent frameworks, platforms, and tooling.
  • Develop reusable agent architectures, prompt libraries, and patterns that accelerate delivery across the consultancy.
  • Contribute to the AI practice’s positioning, thought leadership, and go-to-market efforts with technical credibility.
  • Mentor other engineers on AI patterns, tools, and best practices as the practice grows.

Qualifications

• 2+ years of professional software engineering experience with strong fundamentals.
• 2+ years of hands-on experience building AI agent systems, including autonomous or semi-autonomous agents deployed in production environments.
• Deep working knowledge of agent orchestration frameworks (LangChain, LangGraph, CrewAI, LlamaIndex, Claude Managed Agents, or equivalent). You should have opinions about their tradeoffs.
• Experience with agent design patterns including prompt engineering, RAG architectures, tool/function calling, agent memory management, and evaluation frameworks.
• Experience with MCP (Model Context Protocol), A2A (Agent-to-Agent Protocol), or other emerging agent interoperability standards.
• Demonstrated ability to lead client-facing technical conversations, translate business problems into technical solutions, and present to senior stakeholders.

Nice to Have

• Consulting or agency experience delivering technical projects for external clients.
• Familiarity with infrastructure for AI workloads: vector databases (Pinecone, Weaviate, pgvector), cloud platforms (AWS, Azure, or GCP), and observability tools (LangSmith, PostHog, or OpenTelemetry).
• MLOps experience: comfortable with LLM monitoring, agent observability, eval design, and guardrail implementation to keep agent systems reliable and safe in production.
• Experience building internal tools or automations that improved operational efficiency.
• Hands-on experience with workflow automation and AI agent platforms such as Workato, Cassidy AI, n8n, Claude, or Claude Managed Agents.

Benefits

• Health Insurance (company pays 100% of employee PPO premium)
• Dental Insurance (company pays 100% of employee premium)
• Vision Insurance (company pays 100% of premium)
• 401(k) with up to 4% company match
• $1,000 annual conference budget
• Flexible work and vacation schedule