Lead Machine Learning Engineer

Lead Machine Learning Engineer 📍 Blue Ash, OH (Onsite) 💰 $110,000 - $140,000 + Benefits 🛒 Retail & Consumer Analytics 🕒 Full-Time W2 | 8+ Years Machine Learning Engineering Experience I'm currently supporting a leading enterprise organisation in their search for a Lead Machine Learning Engineer to help drive the next phase of their AI and Machine Learning platform strategy. This is a hands-on technical leadership position focused on building, deploying, and scaling production-grade machine learning solutions. You'll work closely with Data Science, Data Engineering, Cloud, and Platform teams to transform machine learning models into robust, reliable, and business-critical services. The successful candidate will play a key role in shaping MLOps best practices, improving model lifecycle management, and influencing the future direction of the organisation's machine learning platform. Alongside deep technical expertise, you'll be expected to provide leadership, mentor engineers, and help establish engineering standards across the team. Roles & Responsibilities ML Engineering & Delivery • Lead the design and implementation of production ML pipelines for training, batch inference, and real-time or 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 using containers, serverless technologies, and managed endpoints while optimising for performance and cost. MLOps • Implement CI/CD pipelines for machine learning, including model versioning, automated testing, promotion gates, and rollback strategies. • Leverage MLflow for experiment tracking, model registry, and lifecycle management. • Establish best practices for model monitoring, including data drift, concept drift, model degradation, and alerting. • Define and enforce responsible AI standards including bias checks, explainability, privacy controls, and auditability. Data & Platform Collaboration • Partner with Data Engineering teams to ensure data quality, lineage, and availability for reliable model inputs. • Collaborate with Cloud and Platform teams to deliver scalable infrastructure covering compute, networking, security, secrets management, and logging. • Influence architecture decisions and contribute to the long-term machine learning platform roadmap. Leadership & Mentoring • Provide technical leadership and mentorship to Machine Learning Engineers and junior team members. • Conduct design reviews and code reviews while establishing engineering best practices. • Coordinate delivery plans, estimate workstreams, and manage technical risks and dependencies. Required Skills • Strong Python development experience. • Experience with SQL and data processing technologies. • Expertise in MLflow or equivalent MLOps platforms, including model registry, monitoring, and evaluation pipelines. • Strong understanding of Spark, DataFrames, data modelling, and feature engineering. • Experience with Git, CI/CD pipelines, and Docker. • Azure cloud experience, including logging and monitoring solutions. • Strong understanding of MLOps practices including model versioning, monitoring, and automated deployment pipelines. • Kubernetes and Terraform experience are beneficial. Good to Have • Understanding of Data Science models and workflows. • Exposure to deep learning frameworks such as TensorFlow or PyTorch. • Strong knowledge of feature engineering, model evaluation, and experimentation methodologies. Preferred Traits • Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. • Strong storytelling and data-driven decision-making capabilities. • Ability to thrive in a collaborative and fast-paced environment. • Passion for solving complex business problems using data and machine learning. If you have experience building and scaling production machine learning platforms and want to play a key role in shaping enterprise AI capabilities, I'd love to hear from you.