Tech Lead (AI Solutions)

The Tech Lead is responsible for translating product ideas and data science capabilities into production-ready AI solutions. This role partners closely with Product Management and the Data Science Leader to rapidly design, prototype, and ship AI-driven features that deliver measurable business value.

This is a highly hands-on technical leadership role focused on speed, pragmatism, and production quality, balancing experimentation with scalable engineering practices.

Key Responsibilities

AI Solution Delivery & Architecture

• Lead the technical design and implementation of AI-powered product features from concept through production.
• Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration.
• Make pragmatic decisions to accelerate delivery while maintaining system integrity.
• Ensure AI solutions are secure, observable, scalable, and aligned with platform standards.

Pod Leadership & Execution

• Act as the technical lead for a cross-functional AI Pod.
• Break down product requirements into executable technical workstreams and prototypes.
• Guide rapid iteration cycles, proofs-of-concept, and MVPs, balancing experimentation with production readiness.
• Review code, architecture, and technical decisions to maintain quality and velocity.

Product & Data Collaboration

• Partner closely with Product Management to shape problem definitions, success metrics, and delivery plans.
• Collaborate with the Data Science Leader to integrate models, analytics, and data assets into product workflows.
• Translate data science outputs into consumable APIs, services, and product features.
• Provide technical feedback on feasibility, scope, and tradeoffs during product discovery.

Operationalization & Quality

• Ensure features are production-grade, including monitoring, logging, and performance tracking.
• Implement guardrails around AI usage, including reliability, latency, cost controls, and failure modes.
• Support experimentation frameworks, A/B testing, and post-launch learning loops.
• Drive responsible AI practices, including explainability, bias awareness, and data privacy considerations.

Technical Standards & Enablement

• Define and enforce lightweight engineering standards for AI-enabled systems.
• Promote reuse of components, prompts, pipelines, and services across AI initiatives.
• Mentor pod engineers on AI-adjacent system design and best practices.
• Contribute to internal documentation and shared AI patterns/playbooks.