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.
JOB TITLE: Lead Engineer AI Developer Platform
JOB LOCATION: St. Petersburgh, FL (Hybrid)
WAGE RANGE*: $80-100/hr.
JOB NUMBER: 37355611
JOB DESCRIPTION
About the Role
Lead Engineer to own the technical direction of our internal AI-powered developer platform — the AI Dev Kit — which helps engineers across the firm move from idea to production faster by putting a team of specialized AI agents alongside them inside their IDE.
The platform is already in active use by multiple application teams. We are now investing in making it broadly available, easier to install, measurably reliable, and durable against a rapidly evolving AI landscape. We need a senior technical lead to set the architecture, raise the engineering bar, and personally land the most important changes.
This is a hands-on role. You will write code, review pull requests, lead design discussions, respond to production issues, and shape the long-term technical vision in roughly equal measure. The right candidate is energized by building the tools that make every other engineer faster — and is comfortable making decisions in a space where the underlying technology changes every few weeks.
What You'll Do
Set the technical direction.
You will own the architecture of the platform — how it is structured, how new capabilities are added, how it stays portable across multiple AI coding tools, and how it remains usable on Windows, macOS, and Linux without compromise. You will write the patterns the rest of the team follows.
Ship the highest impact work yourself.
A few examples of what is on the near-term roadmap:A single-click install that works identically on Windows and macOS, takes a brand-new engineer from "I have Git” to "I have a working AI dev environment” in under five minutes, and recovers cleanly from partial failures.
A usage and cost telemetry layer that gives finance and engineering leadership a trustworthy view of where AI spend is going, by team, by workflow, by model.
A sandboxed AI development environment where agent-driven changes can be built, tested, and reviewed before they touch a real branch.
Continued expansion of the agent and integration surface — new development workflows, additional enterprise system connectors, deeper knowledge curation for the business domains we support.
Keep the platform current as AI changes.
The model and tooling landscape moves on a 6–12 week cycle. You are responsible for keeping the platform from getting locked into a single vendor or a single approach. That means continuously evaluating new models, new agent patterns, new IDE surfaces, and new orchestration techniques — and being decisive about what to adopt, what to defer, and what to remove. You will run a quarterly capability review with the product owner and translate it into a clear plan.
Raise the engineering bar.
You will own code review for changes to the core of the platform, defend the test and coverage standards, drive incident response when the platform breaks for users, and make sure that the way we build the platform is itself an example of good engineering practice.
Influence the contributors.
You will technically lead and influence contributors from across the technology team, running a federated contribution model that lets application teams co-develop new features. You will run design reviews, pair on hard problems, unblock people, and grow the next generation of senior engineers on the team.
Engage with the engineers who use the platform.
You will hold regular office hours with the application teams adopting the platform, convert their friction into roadmap items, and partner with other stakeholders on what to build next.
Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities
* While an hourly range is posted for this position, an eventual hourly rate is determined by a comprehensive salary analysis which considers multiple factors including but not limited to: job-related knowledge, skills and qualifications, education and experience as compared to others in the organization doing substantially similar work, if applicable, and market and business considerations. Benefits offered include medical, dental and vision benefits; dependent care flexible spending account; 401(k) plan; voluntary life/short term disability/whole life/term life/accident and critical illness coverage; employee assistance program; sick leave in accordance with regulation. Benefits may be subject to generally applicable eligibility, waiting period, contribution, and other requirements and conditions. Benefits offered are in accordance with applicable federal, state, and local laws and subject to change at TCM's discretion.