Technical Lead (AI)

Lead the technical delivery of high-value automation use cases that materially reduce operating costs 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 and establish the automation squads while staying close to hands-on delivery and product management value, ensuring that measurable outcomes translate into real business savings and efficiency gains.

The core responsibilities for the job include the following:

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, and 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. Audit trails and traceability. Reversibility of actions where possible.

Operational responsibilities:

• Define technical evaluation frameworks and 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.