Lead AI Engineer

Lead the technical delivery of high-value automation use cases that materially reduce operating cost and improve business efficiency within a product operating model environment. You will work as part of the Automation Squad, partnering closely with a progress-oriented Product Manager to deliver safe, measurable, and supportable solutions. You will set the technical direction, standards, and guardrails while staying close to hands-on delivery where it adds value, ensuring that squad outcomes translate into real business savings and efficiency gains. This role is available on a remote, hybrid or office-based basis in India working with team in UK Reporting line and key relationships Reports to: Hiring manager (to be confirmed internally).
Responsibilities:
Works closely with (day to day): Product Manager, Developer Engineering, Business outcome owners, Architecture, Data/Information Management, Information Security, Data Protection, Legal/Compliance, and subject-matter experts.
Leadership: Technical leadership and mentoring; may include line management depending on team structure. What success looks like (outcomes).
A predictable delivery cadence from idea discovery to prototype production, with clear acceptance criteria and evidence of business value for each use case.
Measurable reductions in operating cost and improvements in business efficiency (e. g., reduced handling time, error rate, or manual effort) for the processes supported by the squad's AI capabilities.
Reusable components and delivery patterns for generative and agentic AI (design, evaluation, guardrails, deployment, and monitoring) that accelerate future automation work.
Solutions that are secure, compliant, cost-efficient, and operationally supportable.
Transparent decisions: trade-offs (risk, cost, performance, and maintainability) are explicit and backed by evidence. The squad delivers clear and measurable business outcomes, not just technical output.
General responsibilities:
Advise the Product Manager on technical feasibility and cost implications during use case qualification before any commitment is made.
Partner with the Product Manager to shape, refine, and deliver the prioritized backlog, translating needs into testable, outcome-focused acceptance criteria.
Contribute to continuous discovery and innovation, ensuring technical realities and constraints are surfaced early.
Architecture and reusability strategy:
Own model, technical stack selection, orchestration patterns, data pipelines, integration architecture, and security boundaries for squad use cases.
Make explicit build vs. buy vs. reuse decisions for each use case, factoring in total cost of ownership and time-to-value.
Define and maintain the reusable AI component library (prompt patterns, tools/agents, retrieval patterns, libraries, templates) that enables faster, cheaper delivery over time.
Protect architectural integrity, with the authority to delay or reshape delivery where necessary to avoid unsustainable technical or cost debt.
Agentic system design and control:
Define how agents are scoped, constrained, monitored, and governed within the organization's risk appetite.
Enforce non-negotiable principles, including: Scope containment and least-privilege access. Human-in-the-loop checkpoints for material or irreversible decisions, Failure-mode mapping, and safe fallback behaviors. o Audit trails and traceability, Reversibility of actions where possible.
Operational responsibilities:
Define technical evaluation frameworks - how output quality, safety, and business impact (including cost and efficiency) are measured before and after deployment.
Ensure monitoring, alerting, and observability are in place before go-live, including quality, latency, usage, cost, and incident signals.
Own technical risk assessments for every use case, with special emphasis on regulatory, privacy, and safety risk.
Review and approve all technical designs produced by Developers, ensuring alignment with architecture, security, cost, and quality standards.
Manage technical dependencies with data, infrastructure, and security teams, ensuring smooth integration and sustainable operations.
Lead technical incident response where AI solutions are implicated, ensuring root causes are addressed and learnings are fed back into patterns and standards.
Continuously optimize model, infrastructure, and integration choices for cost-effectiveness without compromising safety or required performance.

Experience
8-10 yrs