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 .

Responsibilities

Agentic 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 For

Leadership & 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 incrementally

• Committed to continuous improvement and data-driven, evidence-backed decision-making

• Enthusiastic about knowledge sharing , mentoring , and building high-performing teams

Experience & Skills

Required

• Proven experience leading AI/ML or platform engineering teams in product-focused environments

• Strong hands-on engineering experience in Python and/or Golang — building and shipping production systems

• Demonstrated experience building agentic AI systems in production : multi-agent orchestration , planning and tool-use loops , function calling , RAG pipelines , and frameworks such as LangChain / LangGraph ,