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# Principal Engineer, AI/ML Software

- **Company:** Analog Devices
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $230,475.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Code Review, Continuous Delivery, Extract Transform Load (ETL), Data Transformation, Data Stores, Linux, DevOps, Distributed Computing Environment, Fault Tolerance, Python (Programming Language), Machine Learning, Tensorflow, Prometheus, Azure Machine Learning, Shell Script, Software Systems, Google Cloud, Data Ingestion, Pytorch, Grafana, Model Validation, Multi-Cloud, Cloudformation, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Machine Learning Operations, Restful APIs, Terraform, Docker, Microservices
- **Published:** July 14, 2026
- **Apply:** https://www.oriontalent.com/search-jobs/career/11626772/principal-engineer-ai-ml-software-massachusetts-ma-boston

## About the Role

Requirements: Must have a Bachelor's degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and five (5) years of experience as a software engineer building and maintaining machine learning software workflows.

In the alternative, Master's degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and three (3) years of experience as a software engineer building and maintaining machine learning software workflows., * Demonstrated Expertise ("DE") designing, developing, and maintaining end-to-end machine learning (ML) pipelines, including data ingestion, preprocessing, model training, validation, and deployment (using PyTorch or TensorFlow); and managing experiment tracking and model lifecycle with MLflow or CometML;

 * DE in technical leadership of production ML platforms and pipelines-leading a small, cross-functional team; setting standards, running design/code reviews, and mentoring junior engineers;
 * DE building scalable systems on cloud platforms, with hands-on experience designing fault-tolerant architectures, distributed training setups, multi-cloud strategies (using AWS, GCP, or Azure), and automating infrastructure tasks with Linux and shell scripting;
 * DE in containerization, orchestration, and MLOps/DevOps practices, including deploying ML models and pipelines with Docker and Kubernetes; implementing CI/CD and infrastructure-as-code (Terraform or AWS CloudFormation); and setting up monitoring and observability (Prometheus and Grafana);
 * DE developing distributed data processing pipelines for real-time or batch ML workflows (using Apache Airflow and Apache Kafka); and
 * DE leading the design, building, and maintenance of scalable, robust, and secure RESTful APIs and microservices architectures using Python, with knowledge of computer networks and protocols.

## Description

Design, build, and maintain robust MLOps (Machine-Learning Operations) software systems. Support the development, deployment, testing, and monitoring of AI/ML models on modern cloud-native platforms. Collaborate with data scientists, software engineers, and stakeholders to operationalize AI/ML solutions and ensure their production readiness. Implement and maintain ETL pipelines, automated workflows, and scalable data stores. Ensure high standards of model performance, security, and scalability through continuous monitoring and enhancement of software infrastructure. Guide the MLOps technology roadmap and evaluate emerging tools and technologies to enhance platform capabilities. Utilize MLOps frameworks such as Kubeflow and MLflow, and work with containerization and orchestration tools including Docker and Kubernetes. Deploy infrastructure using Terraform and manage cloud-based resources on platforms such as GCP, AWS, and Azure. Contribute to Agile development processes and cross-functional
team collaboration.


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