LEAD ML ENGINEER

Lead Ml Engineer

Location: Blue Ash, OH (Onsite)

Duration: Full-time only

Job Description: Lead and (“Machine Learning” or “ML”) and “Python” and (“MLOps” or “MLflow” or “ML ops” or “Ml-ops” or “ML-flow” or “ML flow”) and (“Docker” or “Kubernetes” or “Terraform”) and (“Azure”) and (“Spark” or “DataFrames” or “data modelling”)

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.

If you are interested, please send me your updated resume ASAP with below details :

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Looking forward for your response..

Thanks and Regards,

Harsh Srivastava

Jconnect Infotech Inc.