- Location: Building 300-Whippany Campus, Jefferson Park
- This is a very senior, hands-on technical leadership role for a data scientist
- who has built real, production AI applications for internal teams and for
- customers and knows what it takes to take an idea all the way from
- prototype to production within a regulated environment.
- You'll sit at the intersection of our business, our users, and the bleeding
- edge of AI. You won't be handed a spec you'll work directly with
- stakeholders to understand the problem, choose the right approach for it,
- build the solution, deploy it, and stay with it as it grows. You are the
- person who turns ambiguous goals into production agentic systems that people
- depend on, and who keeps us ahead of a technology landscape that changes week
- to week.
WHAT YOU'LL DO
- Take ideas from prototype to production. Start from a rough concept or
- business problem, prove it quickly with a prototype, then do the hard
- engineering to turn it into a reliable, scalable, production-grade system
- people can depend on — and know what each stage demands.
- Build agentic AI, end to end. Design, implement, deploy, and evolve
- autonomous and tool-using AI systems — agents that reason, plan, call
- tools, and act on the user's behalf — for both internal and
- customer-facing use, owning the full lifecycle and the outcome.
- Design agentic architectures. Build agent-orchestration systems: single
- and multi-agent setups, tool and function calling, Model Context Protocol
- (MCP) and agent-to-agent coordination, planning and reasoning loops,
- memory, identity and access for agents, and the control and safety
- mechanisms that keep autonomous behaviour trustworthy.
- Match the solution to the problem. No one-size-fits-all. You assess each
- problem on its own terms and choose the right approach — a different
- model, agent pattern, or architecture for a different need — rather than
- forcing everything through a single template.
- Live on the bleeding edge. Actively track new models, agentic frameworks,
- techniques, and tooling as they emerge; prototype quickly, form a view,
- and bring the ones that matter into production with sound judgement about
- what's ready and what's hype.
- Deliver, dependably. Ship software to production on time and to a high
standard, navigating the controls, governance, and compliance
expectations of a regulated environment.
- Embed with the business. Work shoulder-to-shoulder with stakeholders,
shape the roadmap, and translate business goals into working AI
capability.
- Set the standard. Define how agentic AI is built here — quality,
- evaluation, observability, and responsible-AI practices that others
- follow — and be the trusted technical authority for AI delivery across
- the organisation.
ESSENTIAL (MUST-HAVE)
- Building AI/LLM applications in production — real systems people rely on,
whether internal or customer-facing, not prototypes or demos.
- Taking ideas from prototype to production — a proven ability to move from
a fast proof of concept to a hardened, scalable, maintainable production
system, and the judgement to know what changes between the two.
- A proven track record of delivering software to production in a regulated
- environment — real, shipped systems with lasting impact, delivered within
- governance, risk, compliance, and audit controls (e.g. financial services
- or similar) without losing delivery pace.
- Hands-on experience with agentic AI — building systems where models
- reason, plan, call tools, and take actions, including multi-agent and
- agent-to-agent (A2A) coordination, tool and data integration via the
- Model Context Protocol (MCP), and the control and safety mechanisms that
- keep autonomous behaviour trustworthy.
- Identity and access for AI — how agents authenticate, act on behalf of
- users with delegated authority, and operate under non-human/service
- identities, with the authentication, authorisation, and auditability a
- regulated environment demands.
- Strong software engineering fundamentals — reliable, observable, testable,
maintainable systems.
- Excellent communication and stakeholder skills — fluent moving between a
business conversation and a technical design, and comfortable working
directly with non-technical stakeholders.
- A forward-deployed mindset — comfortable with ambiguity, close to the
business and the user, biased toward shipping and iterating.
STRONGLY DESIRABLE
- Retrieval-augmented generation (RAG) — grounding responses in trusted
organisational knowledge.
- Evaluation (evals) — defining, measuring, and improving the quality and
reliability of AI and agent behaviour.
- Guardrails and safety — input/output filtering, hallucination mitigation,
control over autonomous actions, and responsible-AI practices.
- Prompt and context design, memory, and other techniques for shaping
reliable model and agent behaviour.
- Choosing the right approach for the problem — range and judgement to
select between different models, agent patterns, and architectures as the
problem demands.
- Leading architecture across teams — setting technical direction that other
engineers build on.
- An insatiable pull toward new and emerging technology — you keep up with
the frontier because you want to, and you can tell production-ready from
hype.
NICE TO HAVE
- Experience building customer-facing AI — delivering AI systems that real
external customers interact with, and handling the extra bar for safety,
trust, and polish that comes with it.
- Exposure to agent-driven and AI-generated UI — interfaces that agents
render, drive, or adapt dynamically.
All Senior Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
Minimum Salary: $235,000
Maximum Salary: $285,000
The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.
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Additional Locations: New York, 43 West 23rd Street
Job description provided by Barclays.
View the original posting