Description review
Tech Lead
Enumerate · Latin America - Remote · back to the listing
HR standards
71/100
solid
Title ↔ description
65/100
needs work
Reads as
Machine Learning Engineer
92% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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Startups
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What the listing never says
- 32 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
Total monthly compensation:
$3,500—$4,500 USD
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.
Total monthly compensation:
$3,500—$4,500 USD