Lead AI Engineer, Data Solutions

Salesforce Salesforce · Enterprise · San Francisco, CA +3

Lead AI Engineer focused on building next-generation AI and ML systems, specifically intelligent decisioning systems and an agent flywheel for continuous improvement. The role involves developing ML models and AI agents, owning data and model pipelines, and implementing evaluation frameworks for production systems. It requires strong Python, ML deployment, data pipeline, and LLM/agent experience.

What you'd actually do

  1. Design feedback loops that enable agents and ML systems to improve from real-world outcomes
  2. Track outcomes (engagement, conversion, quality) and evaluate agent performance
  3. Build pipelines that collect and structure agent traces into training and evaluation datasets
  4. Drive continuous improvement via prompting, policies, model selection, and fine-tuning
  5. Build and deploy ML models (classification, ranking, forecasting, recommendation)

Skills

Required

  • Python
  • ML model deployment
  • Data pipeline development (ETL/ELT, batch or streaming)
  • API and backend systems development
  • LLM-powered systems (prompting, orchestration, evaluation)
  • Agent workflows and tool usage
  • Supervised learning and evaluation methods
  • A/B testing and experimentation

Nice to have

  • Spark
  • Airflow/Dagster
  • Snowflake/BigQuery
  • Agent improvement systems (scoring, optimization loops)
  • Evaluation tools (e.g., LangSmith, Braintrust)
  • Large-scale experimentation platforms
  • Enterprise SaaS or CRM experience

What the JD emphasized

  • 6+ years in AI/ML engineering or applied data science
  • Proven experience building and deploying ML models
  • Experience building data pipelines (ETL/ELT, batch or streaming)
  • Experience with LLM-powered systems (prompting, orchestration, evaluation)
  • Experience with evaluation loops, agent traces, or iterative improvement systems

Other signals

  • build intelligent decisioning systems
  • building an agent flywheel
  • continuous learning in production
  • design AI agents that combine LLM reasoning, tool usage, and ML decisioning
  • multi-step reasoning, tool orchestration
  • offline and online evaluation frameworks
  • continuous optimization