Lead AI Engineer, Data Solutions

Lead AI Engineer – Salesforce Lead AI Engineer at Salesforce responsible for building next‑generation AI and ML systems that power intelligent decision‑making and agent flywheels for customer success. Salesforce is the #1 AI CRM, where human agents and AI work together to deliver customer success. We value innovation and aim to empower teams with AI tools. 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 (LangSmith, Braintrust, or similar) Experience with large‑scale experimentation platforms Familiarity with enterprise SaaS or CRM What Success Looks Like Agents and ML models improve continuously via feedback loops Reliable data and evaluation pipelines power the agent flywheel Measurable impact on business metrics (conversion, revenue, efficiency) Fast, safe iteration enabled by strong evaluation systems Equal Opportunity Salesforce is an equal opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. Hiring decisions are made on the basis of merit, irrespective of race, religion, color, national origin, sex, sexual orientation, gender identity or expression, disability, veteran status, age, or any other characteristic protected by law. All evaluations and decisions are based on qualifications and performance. #J-18808-Ljbffr salesforce.com, inc.