The Senior MLOps Engineer will design and implement CI/CD pipelines, develop Infrastructure as Code, and manage ML deployment for SageMaker workloads in collaboration with data teams.
About ProCogia:
We help businesses transform data into real growth!
Our clients operate in high-stakes, highly regulated industries (such as telecom, financial services, life sciences, and more), where precision, compliance, and measurable outcomes are non-negotiable. We partner with them by embedding expert data science, engineering, and AI talent directly into projects that matter.
We’re a diverse, close-knit team with a shared goal: delivering top-class, end-to-end data solutions. We don’t just analyse data, we push the boundaries of what’s possible, helping clients unlock new value and insights.
When you join ProCogia, you’ll find a supportive, growth-driven environment where your ideas are welcomed, and your development is prioritized. We offer competitive salaries, generous benefits and perks for personal and professional development.
If you’re ready to unleash your potential and work at the cutting edge of data consulting, we’d love to meet you!
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
Location: Atlanta, GA or Minneapolis, MN
Time Zone: Eastern Time (ET)
Job Description:
We are seeking a Senior MLOps 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:
- 2+ years of experience in MLOps, with 4+ 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.
Top Skills
Amazon Sagemaker
Apache Iceberg
Aws Cdk
Aws Glue
Aws Lambda
Data Engineering
Fastapi
Gitlab Ci
GraphQL
Mlops
Opentable
Parquet
Pydantic
Python
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