ABOUT PROCOGIA:

We’re a diverse, close-knit team with a common pursuit of providing top-class, end-to-end data solutions for our clients. In return for your talent and expertise, you will be rewarded with a competitive salary, generous benefits, alongwith ample opportunity for personal development. ‘Growth mindset’ is something we seek in all our new hires and has helped drive much of our recent growth across North America. Our distinct approach is to push the limits and value derived from data. Working within ProCogia’s thriving environment will allow you to unleash your full career potential.
The core of our culture is maintaining a high level of cultural equality throughout the company. Our diversity and differences allow us to create innovative and effective data solutions for our clients.
Our Core Values: Trust, Growth, Innovation, Excellence, and Ownership

JOB DESCRIPTION:

We are seeking a Senior DevOps Engineer with deep expertise in AWS CDK, MLOps, and data engineering tools to join a high-impact team focused on building reusable, scalable deployment pipelines for Amazon SageMaker workloads. This role combines hands-on engineering, automation, and infrastructure expertise with strong stakeholder engagement skills. You will work closely with data scientists, ML engineers, and platform teams to accelerate ML productization using best-in-class DevOps practices.

Key Responsibilities:

• Design, implement, and maintain reusable CI/CD pipelines for SageMaker-based ML workflows.
• Develop infrastructure as code using AWS CDK for scalable and secure cloud deployments.
• Build and manage integrations with AWS Lambda, Glue, Step Functions, and OpenTable formats (Apache Iceberg, Parquet, etc.).
• Support MLOps lifecycle: model packaging, deployment, versioning, monitoring, and rollback strategies.
• Use GitLab to manage repositories, pipelines, and infrastructure automation.
• Enable logging, monitoring, and cost-effective scaling of SageMaker instances and jobs.
• Collaborate closely with stakeholders across Data Science, Cloud Platform, and Product teams to gather requirements, communicate progress, and iterate on infrastructure designs.

• Ensure operational excellence through well-tested, reliable, and observable deployments.

  Required Skills:

• 3+ years of experience in DevOps or Cloud Engineering, ideally with a focus on machine learning workloads.

• Hands-on experience with GitLab CI Pipelines, artifact scanning, vulnerability checks, and API management.
• Experience in Continuous Development, Continuous Integration (CI/CD), and Test-Driven Development (TDD).
• Experience in building microservices and API architectures using FastAPI, GraphQL, and Pydantic.
• Proficiency in Python v3.6 or higher and experience with Python frameworks such as Pytest.
• Strong experience with AWS CDK (TypeScript or Python) for IaC.
• Hands-on experience with Amazon SageMaker, including pipeline creation and model deployment.
• Solid command over AWS Lambda, AWS Glue, OpenTable formats (like Iceberg/Parquet), and event-driven architectures.
• Practical knowledge of MLOps best practices: reproducibility, metadata management, model drift, etc.
• Experience deploying production-grade data and ML systems.
• Comfortable working in a consulting/client-facing environment, with strong stakeholder management and communication skills.

PREFERRED QUALIFICATIONS:

• Experience with feature stores, ML model registries, or custom SageMaker containers.
• Familiarity with data lineage, cost optimization, and cloud security best practices.

• Background in ML frameworks (TensorFlow, PyTorch, etc.).

  Education:

• Bachelor’s or master’s degree in any of the following: statistics, data science, computer science, or another mathematically intensive field.

ProCogia is proud to be an equal-opportunity employer. We are committed to creating a diverse and inclusive workspace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status

• Design, implement, and maintain reusable CI/CD pipelines for SageMaker-based ML workflows.
• Develop infrastructure as code using AWS CDK for scalable and secure cloud deployments.
• Build and manage integrations with AWS Lambda, Glue, Step Functions, and OpenTable formats (Apache Iceberg, Parquet, etc.).
• Support MLOps lifecycle: model packaging, deployment, versioning, monitoring, and rollback strategies.
• Use GitLab to manage repositories, pipelines, and infrastructure automation.
• Enable logging, monitoring, and cost-effective scaling of SageMaker instances and jobs.
• Collaborate closely with stakeholders across Data Science, Cloud Platform, and Product teams to gather requirements, communicate progress, and iterate on infrastructure designs.
• Ensure operational excellence through well-tested, reliable, and observable deployments