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.
What you will be doing
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.
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