AI Agent & Infrastructure - Lead Engineer

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.

Overview:

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.

What We're Looking For:

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.

Key Responsibilities:

AI Agent Development & Automation

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

Agent Infrastructure & Platform:

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

Observability & Performance Analytics:

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

Collaboration & Impact:

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

Full-stack technical proficiency:

Languages: Java, Python, JavaScript/TypeScript

Frameworks: Angular, Spring Boot

CI/CD platforms and cloud infrastructure (AWS)

Monitoring/observability tools (e.g., Prometheus, Grafana, CloudWatch)

Education/Experience Requirements:

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 DevOps and CI/CD technologies

Strong experience test automation and unit testing

Strong experience software security

Working Conditions:

Hybrid work environment, with defined in-person presence requirements.

Occasional travel and extended hours may be required.