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.
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