Tech Lead - AI & Automation

The Tech Lead for the AI Pod 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.

This is a remote opportunity. We are seeking contractors located in LATAM who are comfortable working in an English-speaking professional environment.

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 AI 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.

Required Qualifications

• BS or MS in Computer Science, Engineering, or related technical field.

• 8+ years of software engineering experience, including leading complex systems.

• Strong experience designing and building production APIs and backend services.

• Hands-on experience integrating ML models or LLMs into production systems.

• Proficiency in Python and at least one backend language (e.g., Java, Node.js, Go).

• Experience with cloud-native architectures (AWS, GCP, or Azure).

• Solid understanding of data pipelines, model serving, and system observability.

• Ability to work closely with product teams in fast-moving, iterative environments.

Preferred Qualifications

• Experience working in AI-first or data-driven product teams.

• Familiarity with modern LLM platforms, prompt engineering, and agent frameworks.

• Experience operationalizing ML models (model serving, monitoring, versioning).

• Exposure to experimentation platforms, feature flags, and A/B testing.

• Experience in Agile or product-led development environments.

What Success Looks Like

• AI features move from idea to production quickly and reliably.

• Product teams trust the AI Pod to deliver pragmatic, high-impact solutions.

• Data science outputs are effectively operationalized into real user workflows.

• AI systems are observable, maintainable, and cost-effective.

• The organization builds confidence and momentum around AI innovation.