Role Description
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
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AI Solution Delivery & Architecture
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Lead the technical design and implementation of AI-powered product features from concept through production.
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Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration.
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Make pragmatic decisions to accelerate delivery while maintaining system integrity.
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Ensure AI solutions are secure, observable, scalable, and aligned with platform standards.
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Pod Leadership & Execution
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Act as the technical lead for a cross-functional AI Pod.
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Break down product requirements into executable technical workstreams and prototypes.
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Guide rapid iteration cycles, proofs-of-concept, and MVPs, balancing experimentation with production readiness.
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Review code, architecture, and technical decisions to maintain quality and velocity.
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Product & Data Collaboration
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Partner closely with Product Management to shape problem definitions, success metrics, and delivery plans.
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Collaborate with the Data Science Leader to integrate models, analytics, and data assets into product workflows.
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Translate data science outputs into consumable APIs, services, and product features.
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Provide technical feedback on feasibility, scope, and tradeoffs during product discovery.
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Operationalization & Quality
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Ensure features are production-grade, including monitoring, logging, and performance tracking.
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Implement guardrails around AI usage, including reliability, latency, cost controls, and failure modes.
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Support experimentation frameworks, A/B testing, and post-launch learning loops.
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Drive responsible AI practices, including explainability, bias awareness, and data privacy considerations.
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Technical Standards & Enablement
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Define and enforce lightweight engineering standards for AI-enabled systems.
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Promote reuse of components, prompts, pipelines, and services across AI initiatives.
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Mentor pod engineers on AI-adjacent system design and best practices.
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Contribute to internal documentation and shared AI patterns/playbooks.
Qualifications
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BS or MS in Computer Science, Engineering, or related technical field.
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5+ years of software engineering experience, including leading complex systems.
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Strong experience designing and building production APIs and backend services.
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Proficiency in Python and at least one backend language (e.g., Java, Node.js, Go).
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Experience with cloud-native architectures (AWS, GCP, or Azure).
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Solid understanding of data pipelines, model serving, and system observability.
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Ability to work closely with product teams in fast-moving, iterative environments.
Preferred Qualifications
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Experience working in AI-first or data-driven product teams.
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Familiarity with modern LLM platforms, prompt engineering, and agent frameworks.
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Experience operationalizing ML models (model serving, monitoring, versioning).
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Exposure to experimentation platforms, feature flags, and A/B testing.
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Experience in Agile or product-led development environments.
Compensation
Total monthly compensation: $3,300 β $4,000 USD