MLOps Lead Engineer

Tiger Analytics; St. Louis, Northern (MO, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Tiger Analytics in St. Louis is seeking an experienced MLOps Lead Engineer to own enterprise ML operations on the Databricks Lakehouse platform. You will design, automate, and govern end-to-end ML production lifecycles—spanning CI/CD/CT, central governance, and real-time observability.

You will act as the principal technical authority, bridging platform engineering with client-side operations by leading upskilling initiatives for onsite engineering teams.

Qualifications

• 8+ years in Data Engineering, Machine Learning Engineering, or DevOps.
• 3+ years leading Databricks MLOps/Data platform implementations.
• Advanced proficiency in Azure Databricks / AWS Databricks, PySpark, MLflow, Delta Lake, Unity Catalog, and Python.

Responsibilities

• Own enterprise ML operations on the Databricks Lakehouse platform.
• Design, automate and govern end-to-end ML production lifecycles (CI/CD/CT, governance, observability).
• Bridge platform engineering with client-side operations and lead upskilling for onsite teams.

Skills

Databricks MLOps leadership

Azure Databricks / AWS Databricks

PySpark

Python

CI/CD/CT pipelines

Tools

Databricks Asset Bundles (DABs)

Azure DevOps

GitHub Actions

Key Vault/KMS

Job description

We are seeking an experienced MLOps Lead Engineer to take full ownership of our enterprise Machine Learning operations on the Databricks Lakehouse platform. In this role, you will design, automate, and govern end-to-end ML production lifecycles—spanning continuous integration and deployment (CI/CD/CT), central governance, and real-time observability. You will act as the principal technical authority, bridging platform engineering with client-side operations by leading upskilling initiatives for onsite engineering teams.

Required Qualifications & Experience

• Experience: 8+ years in Data Engineering, Machine Learning Engineering, or DevOps, with 3+ years specifically leading Databricks MLOps/Data platform implementations.
• Core Technical Stack: Advanced proficiency in Azure Databricks / AWS Databricks, PySpark, MLflow, Delta Lake, Unity Catalog, and Python.
• Automation & DevOps: Strong experience building automated CI/CD/CT pipelines using Databricks Asset Bundles (DABs), Azure DevOps, or GitHub Actions.
• Governance & Security: Deep understanding of enterprise security controls, secret management (Key Vault/KMS), and multi-environment deployment isolation.
• Stakeholder Management: Proven track record in a client-facing technical lead role, with strong communication skills to drive enablement, workshops, and technical handovers.

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.