Senior MLOps Engineer

Your Opportunity 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 within our AWS-based data lakehouse ecosystem. Working alongside data engineers, data scientists, and DevSecOps teams, you will operationalize ML models and ensure the reliability, security, and scalability of the ML lifecycle-from data ingestion through training, deployment, and monitoring. You will help shape the ML Ops framework, 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 within a distributed agile environment. What You'll Do - 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, CloudFormation) to automate ML pipeline infrastructure. - Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker 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. Your Team: This role is part of the Data & AI organization, focusing on the operationalization of ML models and pipelines within 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 - Continuous improvement of ML lifecycle automation About You - 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, TensorFlow, or Scikit-learn. - Hands-on experience with AWS services (SageMaker, 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 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 drift detection tools (CloudWatch, or custom solutions). - Understanding of data lakehouse technologies such as Apache Iceberg or Delta Lake. - Contributions to open-source ML Ops or DevOps tooling. - Experience in Agile development environments and cross-functional collaboration. Pay Range USD $120,120.00 - USD $171,600.00 /Yr. The actual salary offer will carefully consider a wide range of factors including your skills, qualifications, education, training, and experience, as well as the position's work location. Our Work Policy: We value the balance of remote focus and in-person connection. For all our roles not designated as onsite, candidates living within a 30-mile radius of one of our hub office locations (Ipswich, MA; Birmingham, AL; Contoocook, NH) are required to be on-site one day designated by their team for collaboration and syncs. If you reside outside of this 30-mile range, the position is considered fully remote.