MLOps Lead Engineer
Tiger Analytics Inc. Open · verified Oct 22, 2026 At a glance
Senior ML Platform & Ops role at Tiger Analytics Inc. St. Louis · full-time · no salary stated · we estimate $180,000–$245,000 across the United States.
Tiger Analytics Inc. has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Take charge of production ML operations on Databricks Lakehouse, from release workflows through oversight and live monitoring
- Create automated CI/CD/CT pipelines using Databricks Asset Bundles, Azure DevOps or GitHub Actions
- Apply enterprise security measures, handle secrets and keep deployments isolated across environments
- Guide client-facing technical work, preparing onsite teams through training, workshops and handovers
What you bring
- At least eight years in data engineering, ML engineering or DevOps, with three years leading Databricks implementations
- Advanced skill with Databricks on Azure or AWS, PySpark, MLflow, Delta Lake and other listed tools
- Strong background in automated pipelines, enterprise security, secret handling and deployment isolation
- A client-facing technical lead record and communication skills for enablement and handovers
Who this fits
This senior lead role suits someone who can connect platform engineering with client-side operations and guide onsite engineering teams. The job is onsite in St. Louis.
From the employer
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.
Requirements
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.
Benefits
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.