Senior MLOps Engineer

• Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments.
• Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices.
• Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem.
• Develop and maintain integrations between data ingestion, feature stores, and model repositories.
• Apply infrastructure-as-code (Terraform, AWS CDK, Cloud Formation) to automate ML pipeline infrastructure.
• Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or Sage Maker Model Registry.
• Define and automate monitoring, alerting, and retraining strategies for deployed models.
• Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards.
• Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices.
• Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.

• Bachelor's Degree in Computer Science, Data Engineering, or a related technical field or equivalent experience.
• 4+ years of professional experience in software, data, or ML engineering.
• 2+ years of direct experience implementing and maintaining ML pipelines in production.
• Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, Tensor Flow, or Scikit-learn.
• Hands-on experience with AWS services (Sage Maker, Step Functions, Lambda, ECR, S3, Glue, IAM).
• Solid understanding of CI/CD, containerization (Docker).
• Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
• Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or Cloud Formation).
• Strong understanding of data pipelines, ETL/ELT concepts, and feature engineering in a lakehouse environment.
• Proven ability to apply software engineering practices to machine learning workflows.
• Strong communication and collaboration skills across multidisciplinary teams.

Demonstrates expertise in designing and maintaining ML Ops pipelines, utilizing AWS services and infrastructure-as-code tools to automate processes. Strong proficiency in Python and experience with ML frameworks, alongside a solid understanding of CI/CD practices and data engineering principles.

• ML Ops Pipeline Development
• AWS Services (Sage Maker, Lambda, S3)
• Python Programming
• Infrastructure-as-Code (Terraform, Cloud Formation)
• CI/CD Best Practices

• ML Pipeline Implementation
• Python
• AWS CDK
• Terraform
• CI/CD
• Docker
• ETL/ELT Concepts
• Feature Engineering
• ML Frameworks (PyTorch, Tensor Flow, Scikit-learn)
• Model Versioning

• Communication
• Collaboration
• Mentoring

• Bachelor's Degree in Computer Science or Related Field

• Machine Learning
• Data Engineering
• Model Deployment
• Automation
• Compliance

• MLflow
• Sage Maker Model Registry
• Jenkins
• Github Actions
• Data Lakehouse