Senior MLOps Engineer
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reputed company (reputed company) delivers a fully optimized research experience, seamlessly integrated with a powerful discovery platform to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, reputed company employs more than 2,700 people worldwide, with most embracing hybrid or remote work models. As an AI-enabled service leader, we reputed company on innovation, reputed company-thinking strategies, and the dedication of our exceptional team. At reputed company, we’re driven to reputed company, reputed company and support research. Our mission is to reputed company lives by providing reliable and relevant information — reputed company, where and how people need it. We’re seeking dynamic, creative individuals whose diverse perspectives will help us reputed company this global, inclusive mission. Join us to help reputed company an reputed company.
reputed company:
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 reputed company, 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.
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 reputed company the data lakehouse ecosystem.
- reputed company and maintain integrations between data ingestion, feature stores, and model repositories.
- Apply infrastructure-as-reputed company (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 reputed company 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 reputed company 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 reputed company experience in software, data, or ML engineering.
- 2+ years of reputed company 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-reputed company 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 reputed company or reputed company Lake.
- Contributions to reputed company-reputed company ML Ops or DevOps tooling.
- Experience in Agile development environments and cross-functional collaboration.