Senior Engineer, Platform Engineering & AI Enablement

Alkermes; United States; On-site; USD 140,000 - 200,000; Full time

Job summary

Alkermes is building the next generation of cloud, data, and artificial intelligence capabilities to accelerate innovation across the enterprise. Alkermes seeks a Senior Platform Engineer to build and support foundational platforms, automation, and engineering practices enabling secure, scalable delivery of applications, analytics, and AI solutions.

This role focuses on platform engineering foundations, including generative AI deployments, model governance, and collaboration with

Qualifications

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Education

Bachelor's degree in Computer S

Job 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.

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 S