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Tech Lead at Enumerate. . Location: Latin America - Remote. 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 & Qualit. y.  . . . 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..  . . .  . Total monthly compensation:. $3,500. - . $4,500. . USD