Lead Engineer — AI Developer Platform

About the Role:

We are seeking a hands-on Lead Engineer to drive the technical vision and function as a process engineer utilizing our internal AI systems, platforms, and frameworks.

Already adopted by multiple engineering teams, our internal platform is expanding across the firm. We are focused on improving scalability, reliability, ease of adoption, and flexibility as AI technologies rapidly evolve. This role will lead the platform architecture, establish engineering standards, and deliver high-impact technical solutions.

You will spend your time coding, reviewing designs and pull requests, solving complex technical challenges, supporting production environments, and shaping long-term platform strategy. This is an ideal opportunity for a technical leader passionate about building tools that enable engineers to work smarter and faster.

What You'll Do:

Set the Technical Direction

• Define and evolve the platform architecture, ensuring scalability, extensibility, and maintainability.

• Design solutions that remain compatible across multiple AI providers, coding tools, and operating systems.

• Establish engineering patterns and best practices for the broader team.

Deliver High-Impact Solutions

Lead and contribute directly to key platform initiatives, including:

• Streamlining platform installation and onboarding across Windows and macOS.

• Building telemetry and reporting capabilities to provide visibility into AI usage, costs, and adoption.

• Developing secure, sandboxed environments for AI-assisted development and testing.

• Expanding AI agent capabilities, enterprise integrations, and business-domain knowledge resources.

Maintain AI Platform Agility

• Continuously assess emerging AI models, developer tools, and orchestration technologies.

• Recommend and implement new capabilities while avoiding unnecessary vendor dependency.

• Partner with product leadership to evaluate capabilities and define future roadmap priorities.

Raise the Engineering Bar

• Ensure high standards for code quality, testing, reliability, and operational excellence.

• Lead code reviews and architectural reviews for core platform components.

• Support incident response and continuous platform improvements.

Lead Through Influence

• Provide technical leadership across a distributed contributor model.

• Facilitate design reviews, solve complex technical challenges, and mentor engineers.

• Foster collaboration across engineering teams contributing to the platform.

Partner with Platform Users

• Engage regularly with engineering teams using the platform.

• Gather feedback, identify pain points, and translate insights into platform enhancements.

• Collaborate with stakeholders to prioritize investments and improve developer experience.

What You Bring:

• 8+ years of senior engineering experience building enterprise-grade software that other engineers or business teams depend on — internal platforms, large-scale services, developer tooling, SDKs, or similar systems.

• At least 2 years of hands-on experience building solutions powered by large language models (LLMs) or AI agent frameworks.

• Prior experience building or extending agentic coding tools like Claude Code or OpenAI Codex, and IDEs like VS Code — or comparable tools.

• Strong proficiency in Python and working knowledge of Node.js and TypeScript.

• Direct experience designing or substantially extending integrations between AI coding tools and external systems (any of: Model Context Protocol servers, custom LangChain/LangGraph tools, Copilot extensions, JetBrains AI plugins, Cursor / Continue / Aider integrations).

• A working point of view on at least one production AI coding tool — enough to reason intelligently about prompt design, tool-use loops, context window economics, and where these systems tend to fail.

• Experience with more than one LLM provider and a clear position on building provider-agnostic abstractions so the platform isn't trapped on a single vendor's roadmap.

• Cross-platform engineering experience across Windows, macOS, and Linux, including the realities of installers, path handling, shells, and credential storage on each.

• Background in one or more of the technology stacks our application teams use (Python, Java/Spring, Angular, React, .NET, Oracle, Redshift, SQL Server) — enough to be credible reviewing stack-specific contributions.

• A track record of shipping infrastructure that other engineers depend on every day, and being accountable when it breaks.

• Comfort working in a regulated environment — financial services — with the discipline that implies around secrets, data classification, change management, and auditability, and practical experience with the security and data-protection concerns specific to LLM systems: prompt-injection mitigation, secret-leak prevention through prompts and tool outputs, PII redaction, and trust boundaries around model output.

• Deep, hands-on knowledge of the software development lifecycle and modern engineering methodologies — agile, trunk-based development, code review, CI/CD, and observability-driven operations — and experience operating in a mature CI/CD and observability environment (e.g., Azure DevOps, Jenkins, SonarQube, and a log or APM platform such as Splunk, Datadog, or Dynatrace).

• Demonstrated technical leadership without direct authority — you have written architecture decision records that drove real consensus, presented technical direction to senior engineering and business leadership, and mentored other senior engineers (not just juniors) into stronger work.

• Prior work on agent evaluation — golden task sets, regression suites, judge models, or other ways of measuring whether an AI workflow is actually getting better over time.

• Active engagement with the developer-tools, AI agents, or AI/ML open-source community — substantive contributions, conference talks, or published writing that demonstrates depth and a public point of view.

Licenses/Certifications:

• None required.