ML Engineering Delivery

Machine Learning Engineer

Machine Learning Engineering Delivery Location: Blue Ash, OH

Competencies: 10+ years experience required Agile Way of Working Digital: Machine Learning Digital: Artificial Intelligence(AI)

Role Description:

• 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 CICD 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 CloudPlatform 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.

Required Skills:

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

Desirable Skills:

• Knowledge of Retail domain

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

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.