Lead ML Engineer
United IT Solutions
Lead ML Engineer Blue Ash, OH (Onsite) 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.
- Understanding of Data Science models
- Exposure to Deep Learning frameworks such as TensorFlow or PyTorch
- Solid understanding of feature engineering, model evaluation, and experimentation.
- 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
- 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.
- 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.
- 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.
- Provide technical leadership and mentorship to ML Engineers and junior team members.
- Coordinate delivery plans, estimate work, and manage technical risks and dependencies.
Vacancy posted 4 days ago
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