Lead AI Engineer

Title: Lead AI Engineer (W2 Only)

Duration: 6 Months Contract

Client Location: St Petersburg, FL (100% Remote)

Skills:

Required

• 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 two of those years must be deeply hands-on with large language models or agentic systems (this is non-negotiable for this role).
• Prior experience building or extending agentic coding tools like Claude Code or OpenAI Codex, and IDEs like VS Code — or comparable tools.
• Strong Python skills, and enough Node.js / TypeScript to be effective on the parts of the platform that require it.
• 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.