SPECIALIST SEARCH

Production AI Engineering and Technical Leadership Search

For AI-native vendors serving regulated financial institutions.

We recruit individual contributors and technical leaders accountable for production AI outcomes, from hands-on technical ownership to organizational and functional responsibility.

Searches are built around the outcome the hire must own, not title matches alone.

SEARCH COVERAGE

Production AI Engineering and Technical Leadership

Production AI Engineering Search

Individual contributors with direct technical accountability for production AI systems, including the work required to design, deploy, operate, secure, govern, or remediate those systems in regulated environments.

Technical Leadership Search

Technical leaders with substantive technical foundations and organizational, functional, or executive accountability for the teams, systems, and decisions responsible for production AI outcomes.

PRODUCTION AI ENGINEERING

What Production AI Engineering Accountability Looks Like

Production AI engineering is defined by direct technical responsibility for systems that have to work in production. We look for engineers whose work materially shapes system behavior, customer deployment, reliability, security, governance, or corrective action.

Direct technical ownership Hands-on responsibility for production systems, components, integrations, or controls that materially affect outcomes.
Production responsibility Experience working with live systems where reliability, failure handling, performance, and operational consequences matter.
Real operating constraints Technical work performed under customer, security, governance, compliance, or infrastructure constraints relevant to the environment.
Evidence beyond title Demonstrated responsibility for what was built, deployed, operated, or corrected, regardless of whether the title says AI, ML, Platform, Forward Deployed, Security, or something else.

Depending on the mandate, this can include engineers across AI, ML, platform, infrastructure, deployment, security, and related technical functions when their work materially reaches production AI outcomes.

TECHNICAL LEADERSHIP

What Technical Leadership Accountability Looks Like

Technical leadership is not simply a more senior version of individual-contributor engineering. We look for leaders whose responsibility extends beyond their own hands-on work to the teams, systems, technical decisions, and operating mechanisms behind production AI outcomes.

Technical foundation Enough engineering depth to understand the systems, tradeoffs, and production constraints the organization is responsible for.
Organizational responsibility Accountability that extends beyond personal execution to teams, functions, platforms, standards, or operating mechanisms.
Decision authority Real authority over technical direction, priorities, resources, structure, staffing, escalation, or other decisions that materially affect outcomes.
Accountability when outcomes fail Responsibility for addressing failures, correcting underlying mechanisms, and improving how the organization produces or protects production outcomes.

Depending on the mandate, this can include managers, directors, functional heads, VPs, and other technical leaders with comparable authority.

What We Deliver

We give hiring teams a focused search process and clear evidence for evaluating candidates against the actual mandate.

Defined search mandate A clear understanding of the production outcome, technical scope, accountability, and operating constraints the hire needs to carry.
Evidence-backed shortlist A focused group of candidates with demonstrated experience relevant to the role, rather than broad title or keyword matches.
Clear candidate rationale Context on why each candidate fits the search, including relevant ownership, decision authority, operating experience, and potential gaps.
Process support through acceptance Coordination through interviews, feedback, references, offer, and acceptance so the search stays aligned and moving.

How We Think About Production AI Accountability

Design, Deploy, and Operate describe the main accountability surfaces across the production AI lifecycle. Individual contributors usually center on one primary surface, while technical leaders may govern one or several through their organizational scope.

Design Accountability

Accountability for production AI behavior, including evaluation, data and model interfaces, decision logic, and failure modes.

Deploy Accountability

Accountability for integrating production AI into real customer or enterprise environments, including infrastructure, security, compliance, and go-live execution.

Operate Accountability

Accountability for running production AI reliably over time, including monitoring, governance, incident response, cost control, and ongoing production performance.

Out of Scope

We stay deliberately narrow. Jane Michael does not focus on:

Research-only, exploratory, prototype, pilot, or demo-stage AI work.
AI limited to analytics, dashboards, internal tooling, or convenience features.
Roles adjacent to production AI without material technical accountability for production outcomes.
Consumer-first, prosumer, or lightly regulated products.
Companies where regulated financial institutions are incidental rather than core customers.
Companies where AI is not critical to the product's primary value.

Ready to Start a Technical Search?

If you are hiring a production AI engineer or technical leader for an AI-native product serving regulated financial institutions, tell us about the role and the outcomes the hire needs to own.

We'll review the search and respond with next steps within 1 business day.

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