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Lead ML Engineer

United IT Solutions

Cincinnati, OH
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.
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.
Apply
Vacancy posted more than 2 months ago

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