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 _**:
**_Design and implement AI agents that combine: _**:
- **_LLM reasoning _**:
- **_Tool/API usage _**:
- **_ML-based decisioning layers _**:
**_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 blend: _**:
- **_Deterministic logic _**:
- **_Model-based scoring _**:
- **_LLM-driven generation _**:
**_Design systems that scale with increasing agent complexity and data volume _**:
**_6) Platform & API Development _**:
- **_Build scalable Python services and APIs powering agent workflows _**:
- **_Contribute to shared infrastructure for model serving, evaluation, and experimentation _**:
- **_Ensure reliability, observability, and performance of deployed systems _**:
**Qualifications**:
**Core Requirements**:
- **6+ years of experience in AI/ML engineering, applied data science, or closely related roles**:
- **Strong hands-on experience in Python for production systems**:
- **Proven track record building and deploying production-grade ML models**:
- **Strong experience with data pipeline development (ETL/ELT, batch or streaming)**:
- **Experience designing and building AI agents or agent-like systems**:
- **Strong experience with API development and backend services**:
- **Experience with ML lifecycle tooling (training, evaluation, deployment, monitori

📌 Lead Ai Engineer (Ciudad de México)
🏢 Salesforce
📍 Ciudad de México