Director of Engineering, AI Frontiers

Job Description

The Director of Engineering, AI Frontiers is a hybrid management/IC role that leads a small team of AI engineers in evaluating frontier AI technologies for enterprise adoption. The person manages evaluations and prototyping of agentic systems, multimodal models, and AI tooling against real business scenarios while embedding security, compliance, and data governance constraints from the start. They produce working prototypes, reproducible evaluation reports, and evidence-backed recommendations communicated through Radar publications and executive briefings. The role requires 8+ years of engineering experience with 3+ in a lead/management capacity, hands-on LLM/agentic/RAG experience, comfort tracking the frontier, and the ability to translate technical findings for non-technical stakeholders in regulated environments.

The Director of Engineering, AI Frontiers is a hybrid management/IC role that leads a small team of AI engineers in evaluating frontier AI technologies for enterprise adoption. The person manages evaluations and prototyping of agentic systems, multimodal models, and AI tooling against real business scenarios while embedding security, compliance, and data governance constraints from the start. They produce working prototypes, reproducible evaluation reports, and evidence-backed recommendations communicated through Radar publications and executive briefings. The role requires 8+ years of engineering experience with 3+ in a lead/management capacity, hands-on LLM/agentic/RAG experience, comfort tracking the frontier, and the ability to translate technical findings for non-technical stakeholders in regulated environments.

WHAT IS THE OPPORTUNITY?

The AI Frontiers team exists to close the gap between frontier AI capabilities and enterprise adoption. The gap is real: frontier models, agentic platforms, and AI-native tooling advance on a weekly cycle. Internal adoption moves on a quarterly or annual one. The team's mandate is to scan the landscape at the speed it moves, evaluate what matters with enterprise rigor, prove business value through hands‑on prototypes, and produce findings that pull adoption from business lines.

This role leads the technical work of that mission. You will manage a small team of AI engineers, drive hands‑on evaluation and prototyping, build the tooling and infrastructure the team depends on, and ensure that evaluation recommendations are grounded in working code, not vendor demos. You will also be a practitioner yourself: the team practices what it preaches, and every role carries explicit expectations for AI‑augmented work.

WHAT WILL YOU DO?

• Manage and mentor a team of AI engineers across evaluation, prototyping, and tooling work
• Lead hands‑on technical evaluations of frontier AI capabilities, including agentic systems, multimodal models, reasoning systems, and AI development tooling
• Build working prototypes that test capabilities against real business scenarios and enterprise constraints, including security, data governance, and integration requirements
• Establish and maintain sandbox environments for rapid evaluation, validated with risk and security, activated on demand
• Build and operate the tooling that supports the evaluation pipeline: intake, triage, design, execution, and results review
• Partner with liaisons in security, architecture, and risk to embed enterprise constraints into evaluation design from the start, not as an afterthought
• Translate technical findings into clear, evidence‑backed recommendations that business‑line leaders can act on
• Represent the engineering perspective in Radar publications, field work sessions, and executive briefings
• Ensure evaluations are reproducible: methodology, test scenarios, data, and results documented to a standard that a peer could independently assess

WHAT DO YOU NEED TO SUCCEED?

Must have:

• 8+ years of software engineering experience with 3+ years in a technical lead or engineering management role
• Practical experience building and evaluating AI systems, including LLM‑based applications, agentic pipelines, and RAG architectures
• Comfort working at the frontier: you follow model releases, track agentic frameworks, and have opinions about what matters and what is hype
• Strong engineering judgment: you know when to build a prototype in a day and when depth is warranted
• Ability to communicate technical findings in plain language to non‑technical stakeholders
• Experience working in or alongside highly regulated environments, with direct exposure to security, compliance, or governance constraints

Nice‑to‑have:

• Experience evaluating or benchmarking AI models and systems against defined success criteria
• Background in financial services
• Fami