Salesforce is seeking a Lead AI Data Engineer to architect and build next-generation data pipelines and a central knowledge base for the Marketing organization, to support the company's Digital Transformation and deliver customer success
Requirements
- Solid, hands-on expertise in AI/ML, specifically with Large Language Models (LLMs), including fine-tuning, RAG, Context Engineering, Prompt Engineering, and deployment.
- Robust understanding and clear articulation of data engineering principles, database design, tools, and architecture.
- Proficient in Snowflake, Google Big Query, Redshift, or CDP.
- Solid experience with ETL technologies: DBT, IICS, and FiveTran.
- 10+ years of proficiency in SQL, Bash, and Python scripting.
- 7+ years of hands-on experience with Airflow, CI/CD (Jenkins/similar), and GitHub.
- Substantial experience with AWS technologies: ECS, CloudWatch, and Lambda.
Responsibilities
- AI/ML Systems at Scale: Design, build, and deploy production-grade AI/ML systems, with a preference for expertise in agentic systems, search/recommendation platforms, or data-intensive applications.
- LLM & Foundation Model Expertise: Extensive hands-on experience with LLMs, including fine-tuning, RAG, Context Engineering, Prompt Engineering, and deployment.
- Agent & Tooling: Develop systems enabling AI agents to reason, plan, and utilize external tools (APIs, databases).
- Technical Skills: Expert in Python and large-scale data processing technologies.
- Automate ETL in Snowflake and Big Query.
- Construct DBT ETL pipelines for data ingestion and transformation.
- Implement GIT CI/CD for DevOps.
Other
- Bachelor's in Computer Science or related field with 10+ years of progressive experience in data engineering, modeling, automation, and analytics.
- Proven ability to manage multiple high-priority projects and meet deadlines.
- Prior experience mentoring junior team members in a customer-focused environment.
- Experience collaborating closely with Analytics/Data Science teams.
- Must proactively communicate status, identify risks, and drive results with minimal supervision.
