Responsibilities

• Support end-to-end deployment of ML models (batch and real-time) from code validation through to production rollout under guidance from senior team members.
• Work with Data Science teams to facilitate smooth model handover and ensure deployment readiness aligned with implementation standards.
• Build and maintain CI/CD pipelines for model deployment, scoring, and operational monitoring.
• Debug and fix pipeline issues including data ingestion problems, model scoring failures, and deployment errors.
• Write comprehensive tests for ML pipelines (unit, integration, validation) and implement data quality checks and operational monitoring.
• Ensure deployed models meet audit, reconciliation, and governance requirements.
• Monitor production models for operational health, troubleshoot failures, and track data/variable drift over time.
• Work with Platform Engineers within the team to create reusable MLOps templates and support Data Scientists in using them effectively.
• Support model migrations across data sources, tools, systems, and platforms.
• Participate in code reviews, knowledge sharing, and pod activities (standups, grooming, delivery check-ins).
• Learn from senior team members and contribute to continuous improvement of model delivery practices.

Required Skills & Experience

• Solid Python engineering background with some experience in ML model deployment
• Familiarity with AWS services and cloud-based ML deployment (SageMaker experience preferred but not required)
• Basic understanding of data warehousing concepts and SQL (Snowflake experience a plus)
• Experience with or willingness to learn CI/CD tooling (e.g. GitHub Actions), containerization (Docker), and workflow orchestration tools (Airflow/AstroCloud)
• Strong debugging and troubleshooting skills for data pipelines and ML systems
• Experience writing tests (unit, integration) and implementing monitoring/alerting for production systems
• Strong data skills, including the ability to explore and validate datasets to ensure model inputs and outputs are correct
• Basic understanding of ML lifecycle concepts and willingness to learn about model registry, versioning, and deployment practices
• Experience collaborating with Data Science teams or similar cross-functional collaboration
• Understanding of software testing and validation practices, with willingness to learn model-specific governance requirements
• Ability to participate in code reviews and learn from feedback
• Good communication skills with both technical and business stakeholders
• Eagerness to learn and grow in ML engineering and deployment practices
• (Nice to have) Any exposure to MLflow, model monitoring, or MLOps tools
• (Nice to have) Experience with data pipeline tools or frameworks

Personal Attributes

• You're a motivated engineer who enjoys collaborative problem-solving and wants to grow your expertise in ML engineering.
• You care about code quality and are eager to learn about model deployment best practices, auditability, and production systems.
• You communicate well, ask thoughtful questions, and are excited to bridge the gap between Data Science experimentation and production-grade systems.
• You're interested in learning about deployment standards and the audit and reconciliation expectations that come with production ML.
• You're enthusiastic about contributing to automated and self-serve model deployment systems.
• You take initiative, are reliable in your commitments, and value learning from experienced team members.
• You appreciate structure and are committed to developing high standards in both technical delivery and communication.