AI Engineer

Scope: We are looking for an AI Engineer to own and scale the AI and intelligence layer on top of 500s internal data platforms. You will combine machine learning models, LLMs, and agentic systems to transform raw data into actionable intelligence and automation, in order to support decisions to the investment, product and portfolio teams. This is a hands-on senior role with end-to-end ownership of AI systems in production from data inputs and model orchestration to evaluation, reliability, and business impact. Essential Functions: Data & Intelligence Partner with Data Engineering to leverage and enrich internal datasets using ML, LLMs, and external data sources, building enrichment/extraction/normalization pipelines. Build, deploy, and iterate on scoring, ranking, classification, and anomaly-detection models across structured and unstructured data. Own AI/ML logic applied to complex inputs (e.g., PDFs, documents, emails, web data) to generate reliable signals for investment and operational decision-making. AI, Agents & LLM Systems Architect, build, and operate LLM-powered systems and AI agents in production, including RAG, embeddings, vector databases, and tool-using agents. Own prompting and reasoning systems at scale (versioning, testing, reuse) and model orchestration patterns. Optimize for quality, cost, latency, and reliability through model selection, chaining, and evaluation. Product Development & Engineering Collaborate with Product and Engineering to integrate AI systems into internal platforms and workflows. Contribute to internal tools or interfaces when useful, but with a primary focus on AI logic and intelligence layers. Rapidly prototype AI-driven solutions, partnering with other engineers for UI and platform implementation when needed. Quality, Evaluation & Reliability Define and implement evaluation frameworks to measure accuracy, robustness, consistency, and business value of AI/ML outputs. Build automated monitoring and reliability checks to detect regressions, hallucinations, data drift, and model degradation. Establish quality standards and acceptance thresholds, and drive eval-driven iteration across models, prompts, and workflows. Workflow Automation & Integration Design and own end-to-end AI workflows integrated with internal platforms. Partner with Product to shape AI- and ML-powered features from discovery through production rollout. Proactively identify and implement high-leverage opportunities where AI and automation can replace or augment manual processes. Requirements Minimum Qualifications: 5+ years of experience in software, data or AI/ML engineering. Full-stack experience (React, Next.js, Node.js). Proven experience deploying LLMs and ML models in production environments. Strong hands-on experience with: Agentic workflows and LLM-based systems RAG architectures, embeddings, and vector databases Applied machine learning models (classification, scoring, ranking, anomaly detection) Model evaluation, monitoring, and quality control Solid data engineering fundamentals (APIs, SQL/NoSQL, unstructured data). Ability to design AI/ML systems that are observable, testable, and maintainable. Residence in Mexico Preferred Qualifications: Experience in VC, fintech, or analytics-heavy environments. Familiarity with financial or company performance data. Soft Skills Entrepreneurial Mindset: You don't just wait for tickets; you look at the business and propose technical solutions to drive ROI. Strategic Thinking: Ability to translate complex data into simple insights for the investment committee. Agility: Comfortable working in a fast-paced environment where the "tech stack" evolves weekly.

5- 10 years
5+ years of experience in software, data or AI/ML engineering. Full-stack experience (React, Next.js, Node.js). Proven experience deploying LLMs and ML models in production environments. Strong hands-on experience with: Agentic workflows and LLM-based systems RAG architectures, embeddings, and vector databases Applied machine learning models (classification, scoring, ranking, anomaly detection) Model evaluation, monitoring, and quality control Solid data engineering fundamentals (APIs, SQL/NoSQL, unstructured data). Ability to design AI/ML systems that are observable, testable, and maintainable.