Senior Engineer, Platform Engineering & AI Enablement

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.

Basic/Required Qualifications

• 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

The annual base salary for this position ranges from $150,000 to $190,000. In addition, this position is eligible for an annual performance pay bonus. Exact compensation may vary based on skills, training, knowledge, and experience. Alkermes offers a competitive benefits package. Additional details can be found on our careers website:

Alkermes has recently adopted a hybrid working environment to support and meet the needs of employees and this role will operate in a flexible environment with 60% of time in the office and 40% from home. This position is eligible for the hybrid workplace model, requiring work to be completed onsite at our Waltham, MA office at least 3 days per week. This role is not eligible for fully remote work.

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