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
* Youre 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.
* Youre interested in learning about deployment standards and the audit and reconciliation expectations that come with production ML.
* Youre 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
* Youre 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.
* Youre interested in learning about deployment standards and the audit and reconciliation expectations that come with production ML.
* Youre 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.
We work with Textio to make our job design and hiring inclusive.
Permanent
J-18808-Ljbffr