LEAD ML ENGINEER
Posted Updated
Description
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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.
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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.
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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
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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.
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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.
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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.
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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.
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Employee Benefits Summary:
- Discretionary Annual Incentive.
- Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
- Family Support: Maternal & Parental Leaves.
- Insurance Options: Auto & Home Insurance, Identity Theft Protection.
- Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
- Time Off: Vacation, Time Off, Sick Leave & Holidays.
- Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
- 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.
Skills
- AI
- Azure
- Azure DevOps
- CI/CD
- Cloud
- Data Engineering
- Data Modeling
- Data Quality
- Data Science
- Databricks
- Deep Learning
- DevOps
- Docker
- Feature Engineering
- Git
- GitHub
- GitHub Actions
- IAM
- Java
- Kubernetes
- Machine Learning
- MLflow
- MLOps
- Model Evaluation
- Networking
- Observability
- Python
- PyTorch
- Scala
- Serverless
- Spark
- SQL
- TensorFlow
- Terraform
- Test Automation