MLOps Engineer — Player Performance Analytics

Location: 100% Remote
Contract: B2B (via Entrada AI Poland)

ABOUT ENTRADA AI
Entrada AI is a specialized consulting partner and a strategic portfolio company of Databricks Ventures. This is a rare distinction: Databricks invested in us because of our technical excellence, placing us in the "inner circle" of the ecosystem. For our engineers, this means direct access to product roadmaps, private previews, and the teams building the platform.

You will join a team of industry veterans and Databricks MVPs. We look for engineers who value clean architecture over quick fixes. We don't just maintain pipelines; we solve complex architectural challenges for Fortune 500 clients using features often before they are widely available.

The Mission

Our client, a premier professional sports organization, is at a critical technical inflection point. With over 60 machine learning models currently driving player sourcing, development, and evaluation, the organization is migrating its entire production ecosystem from Azure ML Foundry to Databricks.

We aren't just looking for someone to move files; we need a strategic MLOps Engineer to architect the migration framework, automate the "un-automatable," and elevate the organization's MLOps maturity. You will work as an embedded advisor alongside the client’s internal team to ensure that the future of their player analytics is scalable, observable, and rigorous.

What You’ll Do

As a senior-level practitioner, you will move beyond execution into process engineering and advisory. Your impact will be defined by:

• Migration Architecture: Standardize and document a repeatable "migration blueprint" to transition 60+ models from Azure ML Foundry into Databricks without service interruption.

• Automated Validation: Replace manual, incomplete testing with robust, automated infrastructure. You’ll build the frameworks that ensure migrated models perform identically (or better) in their new home.

• Lifecycle Management: Define and implement the long-term maintenance strategy, including sophisticated drift detection, accuracy monitoring, and automated retraining cadences.

• Knowledge Leadership: Act as a peer-coach to the client’s internal MLOps person, transferring knowledge and building a shared, high-maturity capability.

• Cross-Functional Collaboration: Partner closely with the existing embedded Data Engineering team to ensure seamless data flow via Delta Lake and Spark.

Must haves:

• Databricks ML Mastery: Deep experience with the Databricks ecosystem, specifically MLflow (tracking, registry, deployment) and Model Serving.

• Migration Experience: A proven track record of moving production models across platforms (e.g., from SageMaker or Azure ML to Databricks).

• Production Observability: Expertise in building monitoring stacks for drift detection, model health, and real-time alerting.

• ML Testing: Proficiency in building automated validation and integration testing pipelines specifically for ML workloads.

• Azure Infrastructure: Familiarity with Azure ML / Azure ML Foundry as a source platform.

Nice to haves:

• Domain Passion: Experience in or a deep interest in sports analytics (scouting, biomechanics, or player performance).

• Data Fluency: Strong grasp of PySpark and Delta Lake.

• DevOps DNA: Experience building CI/CD pipelines for ML using Azure DevOps.

• Certification: Databricks Machine Learning Professional certification.

THE OFFER

• Competitive Salary: based on your experience and skills.

• Stable Employment: Contract signed with our Polish entity (Entrada AI Poland).

• 100% Remote: Full flexibility to work from anywhere in Poland.

• Apple Hardware: Macbook Air M4 15" provided.

• Referral Bonus: Bonus for bringing other top-tier engineers to the team.

• Professional Growth:

  • Databricks certification cost coverage.

  • Opportunity to present at industry conferences (building your personal brand).

  • Direct collaboration with Databricks MVPs and product teams.