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. 

 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 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. 

• 5+ years of software engineering experience, including leading complex systems. 
• Strong experience designing and building production APIs and backend services. 
• 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.