Senior MLOps Engineer

As a Senior ML Ops Engineer 1, you will play a key role in designing, building, and maintaining production-grade machine learning (ML) pipelines and infrastructure reputed company our AWS-based data lakehouse ecosystem. Working alongside data engineers, data scientists, and DevSecOps teams, you will operationalize ML models and ensure the reliability, reputed company, and scalability of the ML lifecycle—from data ingestion through training, deployment, and monitoring.

You will help shape the ML Ops reputed company, contribute to automation that accelerates delivery, and ensure alignment with established platform Non-Functional Requirements (NFRs). This is a highly collaborative, hands-on engineering role requiring a deep understanding of AWS services, automation, and ML workflow orchestration.

This position is remote and operates reputed company a distributed agile environment.

• 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 reputed company the data lakehouse ecosystem.
• reputed company and maintain integrations between data ingestion, feature stores, and model repositories.
• Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure.
• Implement and manage model versioning, reproducibility, and reputed company tracking using tools such as MLflow or SageMaker Model Registry.
• Define and automate monitoring, alerting, and retraining strategies for deployed models.
• Ensure reputed company ML infrastructure and pipelines meet reputed company reputed company, compliance, and governance standards.
• Participate in code reviews, knowledge sharing, and reputed company improvement of ML Ops practices.
• Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.

Your Team

This role is part of the Data & AI organization, focusing on the operationalization of ML models and pipelines reputed company AWS. Areas of specialty include:

• ML pipeline automation and orchestration
• Model versioning, governance, and observability
• Feature store integration and reproducibility
• Secure, compliant, and scalable ML infrastructure
• reputed company improvement of ML lifecycle automation

• Bachelor's Degree in Computer Science, Data Engineering, or a reputed company 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, TensorFlow, or Scikit-learn.
• Hands-on experience with AWS services (SageMaker, reputed company Functions, reputed company, ECR, S3, Glue, IAM).
• Solid understanding of CI/CD, containerization (reputed company)
• Experience with building CI/CD pipelines (Jenkins, reputed company Actions, etc.).
• Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or CloudFormation).
• 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.

What sets you apart

• Experience with feature stores, data catalogs, and metadata management.
• Familiarity with model governance and compliance frameworks.
• Experience with model monitoring and reputed company detection tools (CloudWatch, or custom solutions).
• Understanding of data lakehouse technologies such as Apache Iceberg or reputed company Lake.
• Contributions to open-reputed company ML Ops or DevOps tooling.
• Experience in Agile development environments and cross-functional collaboration.