AI Agent & Infrastructure - Lead Engineer

Working independently, the Lead Engineer owns development of software products and works on improving the overall quality of the product throughout the software development life cycle and mentors other Software Engineers. Reports directly to a Director or Senior Director.

• Architects and builds the infrastructure and tooling that powers AI agent development across the Software Development Lifecycle (SDLC).
• Develops production‑grade agentic systems, orchestration frameworks, and observability solutions that enable teams to build, deploy, and monitor reliable AI agents at scale.
• Plays a key role in defining and implementing the next generation of SDLC through AI‑first innovation and comprehensive instrumentation.

• You demonstrate sharp product sense for high‑impact automation opportunities, technical taste in implementation decisions, and the ability to clearly articulate trade‑offs. You know when to apply AI agent solutions versus simpler approaches and can explain the “why” behind architectural choices.
• You excel at 0‑to‑1 (and 1‑to‑100) product development, comfortable operating in ambiguous environments where requirements emerge through experimentation and iteration rather than upfront specification.

• Develop production‑grade AI agents that eliminate manual handoffs across the SDLC.
• Create custom integrations and CLI tools that give agents deep understanding of internal systems and codebases.
• Design comprehensive testing strategies to ensure agent reliability and output quality.
• Implement “Golden Path” scaffolding that embeds organizational standards into new projects.
• Build AI solutions that improve codebase navigation, documentation, and developer workflows.
• Identify workflow bottlenecks and deliver measurable impact through intelligent automation.
• Shape SDLC evolution by identifying AI‑first opportunities and proving outcomes through experimentation.

• Architect and maintain production infrastructure supporting agent deployment, lifecycle management, and scaling.
• Develop agent frameworks, templates, and SDKs that accelerate agent development.
• Create governed Model Context Protocol (MCP) catalog enabling compliant agent‑to‑agent and agent‑to‑MCP communication.
• Implement governance controls for agent behavior, permissions, and system access.

• Design and implement metrics, monitoring, and logging infrastructure for AI agents and development workflows.
• Build dashboards that provide actionable insights into developer productivity, tool adoption, and agent performance.
• Establish KPIs and measurement frameworks to quantify the impact of AI‑powered automation.
• Create alerting and anomaly detection systems to ensure reliability of agents and tooling.
• Analyze telemetry data to identify optimization opportunities and guide strategic investment decisions.

• Partner across teams to drive adoption of AI‑powered tooling and process transformation.
• Stay current with LLM technologies and coach colleagues on AI‑assisted development and automation best practices.
• Rapidly prototype solutions to validate use cases and prove value quickly.
• Communicate data‑driven insights to stakeholders through clear visualizations and reports.

• Languages:
  
  Java, Python, JavaScript/Type Script
• Frameworks:
  Angular, Spring Boot
• CI/CD platforms and cloud infrastructure (AWS)
• Monitoring/observability tools (Prometheus, Grafana, Cloud Watch)

• Bachelor’s degree in Computer Science, Information Systems or related discipline with at least 7 years of related experience, or equivalent training and/or work experience.
• Strong system design experience
• Strong experience in object‑oriented development
• Strong experience with cloud technologies
• Strong experience in data storage technologies
• Strong experience in performance tuning and optimization
• Strong experience in Dev Ops and CI/CD technologies
• Strong experience in test automation and unit testing
• Strong experience in software security

• Hybrid work environment, with defined…