Lead AI Engineer, Data Solutions

About the role

The Lead AI Engineer will build next-generation AI and ML systems at Salesforce, focusing 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.

Responsibilities

• 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
• Build ML & Agent Systems
• Design 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

• 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, Braintrust, or similar)
• Experience with large-scale experimentation platforms
• Familiarity with enterprise SaaS or CRM