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Lead AI Engineer, Data Solutions at Salesforce | JobVerse
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Lead AI Engineer, Data Solutions
Salesforce
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Lead AI Engineer, Data Solutions
San Francisco, Illinois, United States of America
Full Time
3 days ago
$172,500 - $260,100 USD
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Key skills
Airflow
BigQuery
ETL
Python
Spark
AI
ML
LLM
ELT
Snowflake
A/B Testing
About this role
Role Overview
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
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 preferred
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.
Tech Stack
Airflow
BigQuery
ETL
Python
Spark
Benefits
time off programs
medical
dental
vision
mental health support
paid parental leave
life and disability insurance
401(k)
employee stock purchasing program
Apply Now
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