LEAD ML ENGINEER Blue Ash, OH FulltimeFTE

REQUIRED SKILLS

• Languages: Python (required); SQL; optional Java/Scala
• ML/MLOps: MLflow (or equivalent) model registry monitoring evaluation pipelines
• Data: Spark DataFrames data modeling fundamentals feature engineering
• DevOps: Git CI/CD Docker; Kubernetes Terraform (optional)
• Cloud: Azure logging/monitoring
• Experience with MLOps practices including model versioning monitoring and CI/CD for ML pipelines.

GOOD TO HAVE

• Understanding of Data Science models
• Exposure to Deep Learning frameworks such as TensorFlow or PyTorch
• Solid understanding of feature engineering model evaluation and experimentation.

PREFERRED TRAITS

• Strong communication and storytelling skills with data
• Ability to work in a collaborative and fast-paced environment
• Passion for solving complex business problems using data

Roles & Responsibilities

ML Engineering & Delivery

• Lead the design and implementation of production ML pipelines for training batch inference and real-time/near-real-time scoring.
• Translate Data Science prototypes into robust maintainable services and workflows with strong testing observability and reliability.
• Build and manage feature engineering workflows feature stores (where applicable) and reusable ML components.
• Drive model packaging and deployment patterns (containers serverless managed endpoints) and optimize for performance and cost.

MLOps

• Implement CI/CD for ML (model versioning automated testing promotion gates rollback strategies) using Azure DevOps / GitHub Actions integrated with Databricks
• Leverage MLflow (Databricks native) for experiment tracking model registry and lifecycle management
• Establish best practices for model monitoring: data drift concept drift model degradation and alerting.
• Define and enforce guardrails for responsible AI: bias checks explainability privacy controls and auditability.

Data & Platform Collaboration

• Partner with Data Engineering on data quality lineage and availability to ensure reliable model inputs.
• Work with Cloud/Platform teams to ensure scalable infrastructure (compute networking IAM secrets logging).
• Influence target architecture and technology decisions for the ML platform roadmap.

Leadership & Mentoring

• Provide technical leadership and mentorship to ML Engineers and junior team members.
• Conduct design reviews code reviews and establish engineering standards.
  • Coordinate delivery plans estimate work and manage technical risks and dependencies.