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Analog Devices · 2 days ago

Senior Engineer, AI/ML Software

Wilmington, MA

Full-time

Onsite

Mid, Senior Level

$109K/yr - $150K/yr

Analog Devices, Inc. is a global semiconductor leader that enables breakthroughs at the Intelligent Edge. The Senior MLOps Engineer will be responsible for designing and optimizing systems for ML/AI operations, ensuring operational excellence, and architecting scalable cloud infrastructure to support ML/AI workflows.

DSPElectronicsLightingSemiconductor

No H1B

U.S. Citizen Only

Responsibilities

Foster and contribute to a culture of operational excellence: high-performance, mission-focused, interdisciplinary collaboration, trust, and shared growth

Drive proactive capability and process enhancements to ensure enduring value creation, analytic compounding interest, and operational maturity of the ML/AI platform

Design and implement resilient cloud-based ML/AI operational capabilities that advance our system attributes: learnability, flexibility, extensibility, interoperability, and scalability

Lead efforts for precision and systemic cost efficiency, optimized system performance, and risk mitigation — with data-driven strategy, comprehensive analytics, and predictive capabilities at both “tree” (component) and “forest” (system) levels of our ML/AI workload and processes

Architect and implement scalable AWS ML/AI cloud infrastructure to support end-to-end lifecycle of models, agents, and services

Establish governance frameworks for ML/AI infrastructure management (e.g., provisioning, monitoring, drift detection, lifecycle management) and ensure compliance with industry-standard processes

Define and ensure principled validation pathways (testing, QA, evaluation) for early-stage GenAI/LLM/Agent-based proofs-of-concept, across the organization

Lead and provide guidance on Kubernetes (k8s) cluster management for ML workflows, including choosing/implementing workflow orchestration solutions (e.g., Argo vs Kubeflow) and data-pipeline creation, management, and governance using tools such as Airflow

Design and develop infrastructure-as-code (IaC) in AWS CDK (in Python) and/or Terraform along with GitOps to automate infrastructure deployment and management

Monitor, analyze and optimize cloud infrastructure and ML/AI model workloads for scalability, cost-efficiency, reliability, and performance

Collaborate with engineering, product, science, design, security and operations teams to translate business requirements into scalable ML/AI solutions, and ensure smooth integration into production systems

Qualification

AWS ML/AI infrastructureKubernetes managementInfrastructure-as-codeData Science LifecycleFeature storesModel governanceMonitoring toolsCloud architectureCommunication skillsProject managementCollaborative mindsetCustomer-obsessed mindset

Required

Deep understanding of the Data Science Lifecycle (DSLC) and proven ability to shepherd data science or ML/AI projects from inception through production within a platform architecture

Expertise in feature stores, model registries, model governance and compliance frameworks specific to ML/AI (e.g. explainability, audit trails)

Experience with monitoring tools for ML/AI (latency/throughput SLAs, model drift, resource usage dashboards)

Expert in infrastructure-as-code and GitOps practices, with demonstrable skills in Terraform, AWS CDK (Python), Argo CD and/or other IaC and CI/CD systems

Hands-on experience managing Kubernetes clusters (for ML workloads) and designing/implementing ML workflow orchestration solutions and data pipelines (e.g., Argo, Kubeflow, Airflow)

Solid understanding of foundation models (LLMs) and their applications in enterprise ML/AI solutions

Strong background in AWS DevOps practices and cloud architecture — e.g., AWS services such as Bedrock, SageMaker, EC2, S3, RDS, Lambda, managed MLFlow, etc. Hands-on design and implementation of microservices architectures, APIs, and database management (SQL/NoSQL)

Proven track record of monitoring and optimizing cloud/ML infrastructure for scalability and cost-efficiency

Excellent verbal and written communication skills — able to report findings, document designs, articulate trade-offs and influence cross-functional stakeholders

Demonstrated ability to manage large-scale, complex projects across an organization, and lead development of major features with broad impact

Customer-obsessed mindset and a passion for building products that solve real-world problems, combined with high organization, diligence, and ability to juggle multiple initiatives and deadlines

Collaborative mindset: ability to foster positive team culture where creativity and innovation thrive

Preferred

Experience with Ray for end-to-end workflows to scale data processing, modeling (training, tuning, serving); and experience with scaling RL is a nice-to-have too!