Senior Cloud Platform Engineer; AWS), AI Infrastructure

Position: Senior Cloud Platform Engineer (AWS), AI Infrastructure - Evinova
WHY JOIN US?
Evinova is a health-tech business focused on accelerating better health outcomes by advancing digital transformation across the life sciences sector. By combining science-based expertise, evidence-led rigor, and deep human insight, we design digital solutions that enable healthcare to work better for everyone.
Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision-making, and ultimately delivering better outcomes for patients worldwide.
At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast-paced, high-impact environment.
We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in-person teamwork, innovation, and meaningful connection.
This role is located in the Greater Toronto Area and follows a hybrid work model. Candidates must reside within commuting distance of the GTA or be willing to relocate for this opportunity.
Introduction to Role   The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is the cloud platform engineering team responsible for building and operating the infrastructure that enables our AI Engineers and Data Scientists to deploy Generative AI applications reliably, securely, efficiently and  a Senior Cloud Platform Engineer on the ML/AI Ops team, you will design, build and operate the AWS platform that runs our production Generative AI, agentic AI and conversational AI workloads.

Most of the code you write will be AWS CDK (Type Script and/or Python) that creates the infrastructure for other teams to build on.
This is a cloud and platform engineering role rather than an AI application development role. You will partner closely with the engineers and Data Scientists who build agents and models, and provide the infrastructure, deployment patterns, model access, observability and operational capabilities they need to move solutions from experimentation into reliable production environments.
You will work across AWS infrastructure, infrastructure as code (IaC), Amazon Bedrock Agent Core, Amazon ECS, CI/CD, Sage Maker Unified Studio, AI gateways, observability, scalability, reliability, security, governance and cost optimization. Your work will establish reusable platform capabilities that allow teams across Evinova to deploy and operate solutions faster and more reliably while meeting the requirements of a highly regulated pharmaceutical environment.
Accountabilities   Cloud Platform Engineering     Design, build and operate scalable AWS cloud platform capabilities for production ML/AI and Generative AI workloads.
Create reusable infrastructure, tooling and deployment patterns that enable AI Engineers and Data Scientists to independently deploy and operate their applications.
Write and maintain AWS IaC primarily AWS CDK in Type Script and/or Python, including reusable CDK constructs that other teams consume.
Build and operate containerized workloads using Amazon ECS and Agent Core.
Develop reusable platform capabilities across compute, networking, IAM, secrets management, storage, model access and workload isolation.
Build and maintain CI/CD and Git Ops workflows that enable safe, automated and repeatable deployments across environments.
Partner with engineers and Data Scientists to transition prototypes and research workloads into resilient, production-grade services.
Build self-services capabilities and automation that improve developer experience and reduce operational toil.
Reliability, Scalability & Operational Excellence     Engineer platform capabilities that improve the availability, scalability, resiliency and performance of production GenAI workloads.
Design and implement autoscaling, load balancing, retries, timeouts, fallback, rate limiting and failure-recovery strategies.
Establish monitoring, alerting, SLOs, runbooks and production-readiness standards for ML/AI workloads.
Troubleshoot complex production issues across AWS infrastructure,…