Senior Engineer, Platform Engineering & AI Enablement

Alkermes•Waltham, MA

3d•$150,000 - $190,000•Hybrid

About The Position

Alkermes is seeking a Senior Platform Engineer to build and support foundational platforms, automation, and engineering practices that enable secure, scalable, and reliable delivery of applications, analytics, and AI solutions. The role contributes to engineering foundations for enterprise AI initiatives, including generative AI applications, model deployment, monitoring, governance, and secure production use. It partners with INDIGO, Security, Infrastructure, Data & Analytics, and delivery teams to simplify development, improve developer productivity, and enable cloud, data, and AI solutions. The position focuses on reusable platform services, developer self-service, standardized workflows, automated software delivery, and secure cloud operations, primarily in AWS. It also develops platform capabilities for AI, machine learning, analytics, and generative AI workloads, helping teams move solutions into reliable production use. The engineer supports operational readiness, platform reliability, documentation, incident response, and continuous improvement, and collaborates across technical and business teams while mentoring less-experienced engineers. The role follows Alkermes’ hybrid workplace model: at least three days per week onsite at the Waltham, Massachusetts office and the remaining time working from home. It is not eligible for fully remote work. The annual base salary is $150,000–$190,000, and the position is eligible for an annual performance pay bonus. Alkermes states that it offers a competitive benefits package; details are available on its careers website.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
At least 5 years of experience in software engineering, platform engineering, cloud engineering, site reliability, or DevOps.
Hands-on experience building or operating cloud platforms, preferably in AWS.
Experience with automated build, testing, deployment, and release pipelines.
Experience implementing infrastructure through code and automation.
Experience with cloud security, monitoring, logging, and operational support.
Experience using scripting or programming languages to automate engineering workflows.
Ability to independently deliver technical solutions within larger cross-functional initiatives.
Experience working with AI, machine learning, data, or analytics platforms.
Ability to troubleshoot complex technical and integration issues.
Experience working in an Agile environment using iterative planning, backlog management, and continuous delivery.
Strong communication skills with technical and nontechnical audiences.

Nice To Haves

Experience with AWS platform services.
Experience with Terraform, Ansible, CloudFormation, or comparable infrastructure automation tools.
Experience with Kubernetes, Docker, or similar container technologies.
Experience with GitLab, GitHub, or another source-control and delivery platform.
Experience with monitoring and observability platforms such as Datadog, Splunk, New Relic, or Grafana.
Experience with Python, PowerShell, Bash, or similar scripting languages.
Experience with Snowflake or other enterprise data platforms.
Familiarity with machine learning operations, generative AI environments, or AI application deployment.
Experience with model monitoring, AI observability, or AI lifecycle automation.
Experience with internal developer platforms, workflow automation, and self-service engineering.
Experience working in a regulated environment; life sciences experience is preferred.
Relevant AWS, Kubernetes, security, or infrastructure automation certifications.

Responsibilities

Build and maintain reusable platform services, automation, and standard engineering patterns.
Develop self-service capabilities and support internal developer platform capabilities and standardized developer workflows.
Automate repetitive activities across cloud, data, AI, and application delivery.
Create and maintain tools, templates, and workflows for architecture intake, risk assessment, and solution reviews.
Implement platform improvements based on agreed architecture and engineering roadmaps.
Build and maintain automated integration, testing, deployment, and release pipelines.
Implement infrastructure through version-controlled code and reusable templates; standardize deployment, configuration, and release processes.
Develop reusable workflows and guardrails that encourage approved engineering practices, and reduce manual effort and developer friction.
Monitor platform adoption, delivery performance, reliability, and quality; maintain technical documentation, operational procedures, and platform guidance.
Build and operate secure, scalable, reliable, and cost-effective cloud platform services, primarily in AWS.
Implement monitoring, logging, alerting, dashboards, and service-health measures.
Participate in incident response, problem investigation, and post-incident improvement activities.
Support disaster recovery, platform patching, upgrades, and vulnerability remediation.
Embed security and compliance controls into platform services and automated delivery workflows.
Evaluate platform performance, reliability, security, and cost, and recommend practical improvements.
Build reusable platform capabilities and cloud patterns for AI, machine learning, analytics, and generative AI workloads.
Partner with data scientists, engineers, and technical teams to move solutions from development into reliable production use.
Automate model and AI application deployment, configuration, monitoring, and lifecycle activities.
Implement approved controls for validation, security, traceability, explainability, and regulatory compliance.
Support AI development environments, shared services, and integration patterns; evaluate emerging technologies and document reusable AI deployment patterns and lessons learned.
Ensure platform services have appropriate documentation, monitoring, support procedures, and ownership.
Assist delivery teams with onboarding, troubleshooting, and adoption of shared platform capabilities; identify support requests suitable for automation or self-service.
Participate in platform health reviews and recommend corrective actions; support capacity, availability, performance, and resilience planning.
Work with service owners to transition new capabilities into sustainable operational support.
Collaborate across Architecture, Engineering, Security, Infrastructure, Product, Data, and business teams.
Participate in technical design reviews and Architecture Review Board preparation; provide input on platform fit, feasibility, supportability, and implementation risk.
Share knowledge, promote consistent engineering practices, and mentor less-experienced engineers through pairing, design discussions, and technical guidance.
Communicate technical issues, risks, dependencies, and recommendations clearly; challenge inefficient practices and propose automation or platform-based solutions.