Agentic AI Engineering Lead — Enterprise Agent Orchestration - SVP

The Department
Developer Engineering is a function of the CTO organization. Our mission is to make it easy and enjoyable for software engineering teams to go from a business idea to delivering an innovative product solution. We are committed to modernizing our toolchain, streamlining delivery processes, automating at scale, and embedding intelligent controls that help engineering teams ship with confidence and speed.
The Team
Within the Developer Engineering department, the Developer Services group is a dedicated expert team at the forefront of the everything-as-code agenda. We exist to deliver measurable reductions in process friction, manual effort, and human error — ensuring our policies, standards, and controls are codified, automated, and consistently applied across the organization.
We hold a unique mandate as part of a greenfield program to shape critical technical consensus at global scale, transforming how the firm's engineers build and apply controls across its technology landscape.
Developer Services is a cross-functional team of engineers, AI practitioners, data scientists, business analysts, and product managers — working together to engineer next-generation codified controls, build the platforms that power them, and drive enterprise adoption.
The Opportunity
This is a rare greenfield / build-from-scratch opportunity to create an enterprise agentic AI capability — the agent orchestration layer, the guardrails, and the adoption model — inside one of the world's most highly regulated financial environments.
As the Agentic AI Engineering Lead, you will own the enterprise rollout of autonomous and semi-autonomous AI agents across the software delivery lifecycle: designing the multi-agent orchestration platform that plans, routes, executes, and audits agent work, and defining the control model that makes AI autonomy safe under financial services regulation. You will start from a blank page, set the technical strategy, lead a team of engineers, and partner with product, security, risk, controls, and platform teams to take agents from pilot to firm-wide production.
You will work hands-on with frontier Generative AI technologies — large language models (LLMs) such as GPT, Gemini, and Claude — through prompt engineering, context engineering, tool / function calling, retrieval-augmented generation (RAG), agentic planning loops, and evaluation harnesses that prove agent behaviour before production.
This is not just an engineering role. It is a leadership opportunity to define how agentic AI is built, governed, and scaled across a global bank.
ResponsibilitiesAgentic AI Platform & Agent Orchestration (Build from Scratch)
Architect and build the enterprise agent orchestration platform from the ground up: agent runtime, planner/executor loops, tool and MCP-style integration layer, memory and context management, state persistence, and human-in-the-loop checkpoints
Design multi-agent patterns — supervisor/worker hierarchies, task decomposition, delegation, hand-off, and recovery — with deterministic fallbacks where autonomy is not appropriate
Build the tool integration surface that lets agents act safely on real enterprise systems: source control, CI/CD, ticketing, service catalogues, data platforms, and internal APIs, each with scoped, least-privilege credentials
Define the evaluation and observability layer: golden datasets, offline and online evals, regression gates on agent behaviour, prompt and model version control, token and cost governance, and end-to-end tracing of every agent decision and action
Establish reusable agent patterns, SDKs, and templates so other engineering teams can build compliant agents without rebuilding the plumbing
Enterprise Rollout & Adoption
Own the enterprise rollout strategy: define the autonomy maturity ladder (assisted → supervised → autonomous), promotion criteria between stages, and the team onboarding path
Prioritize and land high-value agentic use cases across the SDLC — code and change automation, release readiness, change-risk assessment, control evidence generation, incident triage, and developer self-service
Define adoption metrics and prove impact with data: toil eliminated, cycle time, change failure rate, control coverage, cost per outcome
Drive change management and enablement — playbooks, guardrail documentation, office hours, champion networks — so adoption scales beyond early adopters
AI Autonomy in a Highly Regulated Environment
Design the control model for agent autonomy: authorization boundaries, approval and dual-control gates, blast-radius limits, kill switches, deterministic rollback, and segregation of duties between agents and humans
Ensure every agent action is attributable, reproducible, and auditable — full decision and action lineage, immutable audit trails, and evidence artefacts that satisfy internal audit, risk, and regulators
Partner with Risk, Compliance, Model Risk Management (MRM), Information Security, and Legal to move agentic use cases through model governance, risk acceptance, and control attestation
Enforce data residency, entitlement inheritance, PII and confidential-data handling, prompt-injection and data-exfiltration defences, and secure secrets management across every agent path
Codify controls as policy-as-code / compliance-as-code so they apply automatically rather than through manual review
Engineering Leadership
Lead, mentor, and grow a team of AI and platform engineers — fostering technical excellence, psychological safety, and continuous improvement
Collaborate with cross-functional stakeholders — product managers, security engineers, platform teams, controls partners, business analysts — to align the agent platform with organizational goals
Drive cultural change by championing AI-augmented engineering practices and critical, creative thinking about controls and autonomy
Platform & Systems Engineering
Design and build scalable, production-grade backend systems in Python and/or Golang, integrating with cloud-native and containerized infrastructure (Kubernetes / OpenShift)
Ensure all platforms are secure, observable, and compliant with the firm's DevSecOps and SDLC requirements
Champion best practices in system design including distributed systems, event-driven architectures, micro-services, and API-first design
Keep the agent platform anchored in strong delivery foundations — CI/CD, progressive delivery (blue/green, canary), and everything-as-code
What We're Looking ForLeadership & Mindset
A strategic thinker with a hands-on approach — comfortable moving between platform architecture and deep technical delivery
A growth mindset with genuine passion for agentic AI, automation, and engineering innovation
Strong communication and influencing skills — able to carry both engineering teams and senior risk/control stakeholders
An advocate for inclusion, diversity, and psychological safety in all its forms
A self-starter who thrives in ambiguity, brings structure to complexity, and energises the people around them
Ways of Working
Pragmatic and risk-aware, with a creative approach to hard engineering problems — able to ship autonomy incr

Skills
Python, Golang, Microservices, Kubernetes, CI/CD, Generative AI, Risk Management, Onboarding, Documentation