Lead AI Engineer

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Lead AI Engineer (Mexico City) Data Solutions Org Hybrid We are looking for a Lead AI Engineer to drive the development of next-generation AI and ML systems at Salesforce. This role owns the design and evolution of intelligent decisioning systems and expands into building a broader agent flywheel (a system of self-improving feedback loops that continuously evaluate, optimize, and evolve agent performance). This role sits on the applied side but requires strong data and systems engineering depth — you will build not just models and agents, but the data pipelines, evaluation loops, and lightweight system scaffolding that allow them to continuously improve in production. You will build production-grade ML models, embed them into agent workflows, and define how agents learn from real-world outcomes. This is a hands-on, high-impact role focused on shipping systems that directly influence agent performance, efficiency, revenue, and customer experience. What You’ll Do 1) Build the Agent Flywheel Design and implement feedback loops that enable agents and ML models to self-improve over time Develop systems for: Outcome tracking (e.g., engagement, conversions, resolution quality) Agent evaluation (LLM deterministic human-in-the-loop signals) Iterative optimization (prompting, policies, model selection, fine-tuning) Build pipelines that collect and structure agent traces (inputs, tool usage, intermediate steps, outputs) into high-quality training and evaluation datasets Close the loop from production signals → evaluation → model/prompt improvements 2) Develop Production ML & Agent Systems Build and deploy application-specific ML models (classification, ranking, forecasting, recommendation, etc.) Design and implement AI agents that combine: LLM reasoning Tool/API usage ML-based decisioning layers Implement reusable agent patterns (multi-step reasoning, tool orchestration, structured outputs) within application workflows Integrate ML and agent capabilities into decisioning systems that drive business outcomes 3) Data & Pipeline Engineering Design and build scalable data pipelines (batch and near real-time) that power training, evaluation, and inference workflows Develop pipelines that transform raw interaction data into features, labels, and evaluation datasets Partner model pipelines with data pipelines to enable continuous retraining and evaluation loops Ensure data quality, consistency, and availability across systems Work with large-scale structured and unstructured data to support both ML and LLM systems 4) Evaluation, Experimentation & Optimization Build offline and online evaluation frameworks for agent and ML model performance Develop evaluation datasets, golden traces, and regression-style test sets for agent behavior Design and run A/B experiments to measure impact on business outcomes Define and monitor key metrics (quality, containment, revenue impact, latency, etc.) Use production traces and evaluation signals to drive continuous optimization (prompting, model selection, feature improvements, fine-tuning) 5) Architecture & Applied Systems Design Develop hybrid systems that bl ... (truncated, view full listing at source)