Head of AI Lab

WHAT YOU WILL DO

We are establishing the 360F AI Lab, an in-house function tasked with designing, governing, and shipping AI capabilities that materially change how financial advisors and insurers serve their clients. The Head of AI Lab is the senior technical leader and architect of this function.

This is a role for someone who has built and operated AI systems inside regulated financial services. You will own the architectural direction, security posture, cost discipline, IP and knowledge handling, and governance framework of every AI capability that 360F deploys across APAC and the Middle East.

You will set the technical bar, hire and lead the Lab's engineers, decide what we build versus buy, and stand behind those decisions in front of regulators, clients and the board. The role demands judgment honed over years of bridging legacy core systems and modern AI, not enthusiasm for the latest framework.

RESPONSIBILITIES

Strategy &Architecture

•      Define and own the AI reference architecture for the360F platform, including the boundaries between systems-of-record, systems-of-engagement, and the inference layer.

•      Set the technology roadmap for foundation models, agentic frameworks, retrieval systems, and ML Ops tooling with explicit build-vs-buy reasoning.

•      Translate emerging AI capabilities into a sequenced, business-validated portfolio of investments rather than a backlog of experiments.

Security &Governance

•      Own the security posture of all AI systems including prompt injection defenses, agent permission scoping, data residency, and third-party model risk.

•      Establish 360F's model risk management framework: inventory, tiering, validation, monitoring, and decommissioning aligned to MASTRM, MAS FEAT, PDPA, and equivalent APAC requirements.

•      Embed responsible AI principle, explainability, fairness, human-in-the-loop boundaries, and audit trails as design constraints, not bolt-ons.

IP & Knowledge Handling

•      Set the policy and technical controls for what proprietary data leaves the 360F perimeter, under what contractual terms, and to which model providers.

•      Design the institutional knowledge capture layer how underwriting heuristics, advisor scripts, product rules, and claims precedents are codified into governed, access-controlled corpora.

•      Own IP positioning around inputs, outputs, fine-tuned weights, and embeddings, in coordination with Legal and client contracts.

Cost & Engineering Discipline

•      Own the unit economics of every AI capability the Lab ships per-inference cost, retrieval cost, agent run cost and the tooling to monitor them in production.

•      Set the engineering standards for the Lab: CI/CD for AI systems, evaluation harnesses, observability, and the bar for what is allowed into production.

•      Make the hard calls on when AI is the right tool and when a deterministic rule, workflow, or better-designed form is the correct answer.

Leadership &Delivery

•      Hire, mentor, and retain a small, senior AI engineering team. Set the cultural bar for craft, rigor, and intellectual honesty.

•      Partner with Product, Enterprise Architecture, Compliance, and client-facing teams to move capabilities from concept to production at speed.

•      Represent the AI Lab to regulators, clients, partners and the board, bilingually fluent in deep technical detail and senior business language.

REQUIREMENTS

•      12+ years in technology, with at least 7 inside insurance, reinsurance, or financial advisory cross policy administration, underwriting, claims, distribution, or actuarial systems.

•      Track record of designing and shipping AI/ML systems in production within a regulated financial services environment, not pilots or PoCs.

•      Demonstrated ability to bridge legacy core systems and modern AI/cloud stacks. You know that integration and data quality, not models are where transformations actually fail.

•      Deep, current expertise in Generative AI architecture, LLM selection, RAG, agentic orchestration (LangGraph, LangChain, AutoGen orequivalent), vector retrieval, and the trade-offs of fine-tuning vs. prompting vs. retrieval.

•      Strong AI security posture: prompt injection, agent permission scoping, data exfiltration, and model supply-chain risk with controls you have personally implemented.

•      Fluency with regulatory requirements like MAS TRM, MASFEAT, PDPA, and the cross-border data flow realities of operating across APAC and the Middle East.

•      FinOps discipline for AI workloads. You can model unit economics across model tiers, routing strategies, and context-window growth and you have killed projects that didn't pencil out.

•      Clear point of view on IP ownership of inputs, outputs, fine-tuned weights, and embeddings, and how it interacts with vendor contracts and open-source model licenses.

•      Practical experience standing up model risk management and responsible AI frameworks that survive external audit, not slide review.

•      Has hired, mentored, and retained AI engineers in a tight market. Attracts talent on craft and reputation, not only compensation.

•      Bilingual in the rare sense, credible with senior engineers on architecture, and with CFOs, CROs, and regulators on risk, cost and ROI.

•      Stays hands-on with the technology. Personally tests new models, frameworks, and tools as they release, builds prototypes regularly and forms first-hand opinions rather than relying on vendor decks, conference talks or team summaries.

Please submit your resume to [email protected].