Lead AI Engineer, Data Solutions

Salesforce•Chicago, IL

1d•$172,500 - $285,800•Hybrid

About The Position

Salesforce is seeking a Lead AI Engineer to build next-generation AI and ML systems. 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.

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
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
Experience building data pipelines supporting ML systems
Familiarity with tools like Spark, Airflow/Dagster, Snowflake/BigQuery
Understanding of data quality, lineage, and reproducibility
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

Nice To Haves

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

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, and 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, and 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, and 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, and 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, and ensure reliability, observability, and performance.