Lead AI Engineer, Data Solutions at Salesforce

Job description

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. We are looking for a Lead AI Engineer to build next-generation AI and ML systems at Salesforce. This role focuses on developing intelligent decisioning systems and building an agent flywheel —a system of feedback loops that continuously evaluate, optimize, and improve agent performance over time. This is an applied AI role with strong data and systems ownership. You will build models and agents and the data pipelines and evaluation loops that enable continuous learning in production. What You’ll Do Build the Agent Flywheel Design feedback loops that enable agents and ML systems to improve from real-world outcomes Track outcomes (engagement, conversion, quality) and evaluate agent performance Build pipelines that collect and structure agent traces into training and evaluation datasets Drive continuous improvement via prompting, policies, model selection, and fine-tuning Develop ML & Agent Systems Build and deploy ML models (classification, ranking, forecasting, recommendation) Design AI agents that combine LLM reasoning, tool usage, and ML decisioning Implement reusable patterns for multi-step reasoning, tool orchestration, and structured outputs Integrate models and agents into business-critical workflows Own Data & Model Pipelines Design and build scalable data pipelines (batch and near real-time) for training, evaluation, and inference Transform raw interaction data into features, labels, and evaluation datasets Enable continuous retraining and evaluation through tightly coupled data + model pipelines Ensure data quality, consistency, and reliability Evaluation & Experimentation Build offline and online evaluation frameworks Develop evaluation datasets, golden traces, and regression-style test sets Run A/B experiments and track key metrics (quality, revenue impact, latency, etc.) Use production signals to drive continuous optimization Systems & API Development Build scalable Python services and APIs powering agent workflows Collaborate with platform teams while owning application-level systems Ensure reliability, observability, and performance Qualifications Core Requirements 6+ years in AI/ML engineering or applied data science Strong Python experience in production systems Proven experience building and deploying ML models Experience building data pipelines (ETL/ELT, batch or streaming) Experience with APIs and backend systems Agent & LLM Experience Experience with LLM-powered systems (prompting, orchestration, evaluation) Familiarity with agent workflows and tool usage Experience with evaluation loops, agent traces, or iterative improvement systems preferred Data & Systems Expertise Experience building data pipelines supporting ML systems Familiarity with tools like Spark, Airflow/Dagster, Snowflake/BigQuery Understanding of data quality, lineage, and reproducibility Modeling & Experimentation Strong understanding of supervised learning and evaluation methods Experience with A/B testing and experimentation Ability to design systems combining ML, LLMs, and business logic Preferred Qualifications Experience with agent improvement systems (scoring, optimization loops) Exposure to evaluation tools (e.g., LangSmith, Br