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# Senior Engineer, Platform Engineering & AI Enablement

- **Company:** Alkermes, Inc.
- **Location:** Waltham, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $150,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Bash Shell, Cloud Computing, Cloud Computing Security, Cloud Engineering, Continuous Delivery, DevOps, Disaster Recovery, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Windows PowerShell, Reliability Engineering, Cloud Services, Ansible, Software Deployment, Software Engineering, Software Technical Review, Software Vulnerability Management, Enterprise Data Management, Datadog, Data Logging, Scripting, System Availability, Delivery Pipeline, Snowflake, Grafana, Generative AI, Gitlab, Cloudformation, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Data Analytics, Machine Learning Operations, Terraform, Splunk, New Relic (SaaS), Docker, Programming Languages
- **Published:** September 17, 2026
- **Apply:** https://www.careerjet.com/job/us461002f454ae17aab1bfd3fe15cc07f6/eaa

## About the Role

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
5 or more 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 both technical and nontechnical audiences.
Preferred Qualifications/Skills
Experience with AWS platform services.
Terraform, Ansible, CloudFormation, or comparable infrastructure automation tools.
Kubernetes, Docker, or similar container technologies.
GitLab, GitHub, or another source-control and delivery platform.
Monitoring and observability platforms such as Datadog, Splunk, New Relic, or Grafana.
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.
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.
Core Competencies
Hands-on technical execution
Automation mindset
Platform reliability
Security and compliance awareness
Structured problem-solving
Cross-functional collaboration
Clear technical communication
Continuous improvement
Customer and developer focus
Learning agility

## Description

Alkermes is building the next generation of cloud, data, and artificial intelligence capabilities to accelerate innovation across the enterprise. We are seeking a Senior Platform Engineer to build and support the foundational platforms, automation, and engineering practices that enable secure, scalable, and reliable delivery of applications, analytics, and artificial intelligence solutions.
This role will contribute to the engineering foundations supporting enterprise AI initiatives, including generative AI applications, model deployment, monitoring, governance, and secure production use. The engineer will partner with INDIGO, Security, Infrastructure, Data & Analytics, and delivery teams to simplify development processes, improve developer productivity, and enable cloud, data, and AI solutions.
Success in this role will improve delivery speed, platform reliability, developer experience, and the secure adoption of AI technologies across Alkermes.
Responsibilities:
Platform Engineering & Automation
Build and maintain reusable platform services, automation, and standard engineering patterns.
Develop self-service capabilities that help application, data, and AI teams deliver solutions more efficiently.
Support the evolution of 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 supporting architecture intake, risk assessment, and solution reviews.
Implement platform improvements based on agreed architecture and engineering roadmaps.
Software Delivery & Developer Experience
Build and maintain automated integration, testing, deployment, and release pipelines.
Implement infrastructure through version-controlled code and reusable templates.
Help standardize deployment, configuration, and release processes.
Develop reusable workflows and guardrails that make approved engineering practices easier to follow.
Identify opportunities to reduce manual effort and unnecessary developer friction.
Monitor platform adoption, delivery performance, reliability, and quality.
Maintain clear technical documentation, operational procedures, and platform guidance.
Cloud Operations, Reliability & Security
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 to recommend practical improvements.
Artificial Intelligence & Data Enablement
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 that may improve engineering productivity, platform capabilities, or business outcomes.
Document reusable AI deployment patterns and lessons learned for broader adoption.
Platform Support & Operational Readiness
Ensure platform services have appropriate documentation, monitoring, support procedures, and ownership.
Assist delivery teams with onboarding, troubleshooting, and adoption of shared platform capabilities.
Identify recurring support requests that should be addressed through 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.
Collaboration & Technical Contribution
Work across Architecture, Engineering, Security, Infrastructure, Product, Data, and business teams to deliver shared outcomes.
Participate in technical design reviews and Architecture Review Board preparation.
Provide practical technical input on platform fit, feasibility, supportability, and implementation risk.
Share knowledge and promote consistent engineering practices across delivery teams.
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.


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