AI Platform Engineer (Databricks)

Sr. AI Engineer and Mid Level AI Engineer

Number of position- 4

Englewood, CO- 80112

Contract to hire role

Payrate- 67 to 75/hr on W2

Summary

The Sr. Agentic AI Engineer role exists to lead technical onboarding of users, teams, and data domains onto enterprise Databricks platform on AWS. This role solves problems in external data source integration, Unity Catalog governance at scale, deployment of Databricks-native AI capabilities including Genie and Mosaic AI, and cost attribution across onboarding workloads. It is a senior individual contributor position with technical leadership expectations: setting the patterns other engineers follow, mentoring junior engineers, and owning the integration work that makes external data and AI capabilities accessible at enterprise scale. This is a hands-on role, not a management position, that requires deep Databricks platform expertise, production data engineering skill, and practical AI and ML platform knowledge.

Objectives

• Lead the technical onboarding of new users, teams, and business units onto the Databricks platform, defining the patterns other engineers follow and owning the quality bar for what a well-onboarded workload looks like.
• Architect and build integrations connecting enterprise data sources to the platform, serving as the technical lead on integration work that crosses organizational or system boundaries.
• Deploy and operationalize Databricks-native AI capabilities, including Genie and Mosaic AI, and evaluate new Databricks AI features for enterprise fit before recommending adoption.
• Own Unity Catalog architecture across onboarding workloads, setting and holding the governance bar for metadata standards, data classification, and lineage tracking.
• Design and maintain cost attribution and chargeback practices that give engineering leadership the data needed to make resource decisions.
• Set technical direction for onboarding and integration workstreams, provide technical mentorship to engineers, review designs, and represent the platform team in cross-functional technical discussions.

Core Skills and Competencies (What You'll Bring)

• Advanced, hands-on experience administering Databricks at production scale, including Unity Catalog architecture, Delta Lake table design, cluster configuration, and job orchestration.
• Mastery of Python, SQL, and PySpark for building data pipelines that perform reliably under production load and hold up over time.
• Practical AI and ML platform knowledge, including LLM-driven workflows, retrieval-augmented generation, vector search, and agent framework infrastructure.
• Working knowledge of AWS services underpinning the platform, including IAM, S3, and VPC, along with the ability to read and contribute to Terraform configurations.
• Practical experience with platform cost attribution, chargeback design, and usage-based resource management.
• Proven ability to set technical direction, mentor engineers, and represent the platform in cross-functional discussions, backed by strong written and verbal communication for both technical and business audiences.

Additional Qualifications

Successful candidates will typically have:

• Experience with LLM application design, including retrieval-augmented generation and agent frameworks.
• Proficiency in CI/CD for Databricks deployments using Terraform, Asset Bundles, or GitLab.
• Active Databricks certifications in Data Engineering, Platform Administration, or Machine Learning.
• Experience with Databricks Genie, Mosaic AI, or similar Databricks-native AI capabilities.
• Familiarity with enterprise FinOps practices and cost allocation reporting.
• Prior experience in a regulated industry or enterprise environment with formal compliance requirements.
• Experience contributing to or owning an internal developer platform or self-service tooling initiative.

Minimum Requirements

Minimum Education: Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related technical field, or equivalent practical experience

Minimum Experience: 5 or more years of experience in data engineering, platform engineering, or cloud data roles, including at least 3 years of hands-on Databricks production environment management

Required Technical Skills: Must have demonstrated experience implementing Unity Catalog and data governance controls at scale, and proficiency in Python and PySpark for data pipeline design Candidates must be willing to participate in at least one in-person interview.