Machine Learning/AI Operations Architect - Evinova

This team is responsible and accountable for the design, implementation, deployment, health and performance of all algorithms, models, ML/AI operations (MLOps, AIOps, and LLMOps) and Data Science Platform. We manage ML/AI and broader cloud resources, automating operations through infrastructure-as-code and CI/CD pipelines, and ensure best-in-class operations – striving to push even beyond mere compliance with industry standards such as Good Clinical Practices (GCP) and Good Machine Learning Practice (GMLP). 

As the ML/AI Platform Architect on our team, you will architect and oversee the global cloud ML/AI infrastructure that underpins our entire ML/AI value prosposition. You will design, implement, and manage scalable cloud solutions using AWS services while establishing ML/AI governance frameworks, automating infrastructure with tools like AWS CDK and Projen, and conducting cost-benefit analyses of foundation models to drive strategic decisions across the organization.

This position requires a deep understanding of cloud-native ML/AI Ops methodologies and technologies, AWS infrastructure, State-of-the-art (SOTA) Foundation Models and AWS GenAI Services, and the unique demands of regulated industries, making it a cornerstone of our success in delivering impactful solutions to the pharmaceutical industry.

Role & Team Key Responsibilities:

Operational Excellence

• Lead by example in creating high-performance, mission-focusedand interdisciplinary teams/culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people.

• Drive the creation of proactive capability and process enhancements that ensures enduring value creation and analytic compounding interest. 

• Design and implement resilient cloud ML/AI operational capabilities to maximize our system A-bilities (Learnability, Flexibility, Extendibility, Interoperability, Scalability).

• Drive precision and systemic cost efficiency, optimized system performance, and risk mitigation with a data-driven strategy, comprehensive analytics, and predictive capabilities at the tree-and-forest level of our ML/AI systems, workloads and processes.

ML/AI Cloud Operations and Engineering

• Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaSenvironment.

• Establish governance frameworks for ML/AI infrastructure management and ensure compliance with industry best practices.

• Ensure principled and methodical validation pathways and a Well Architected Framework for Embryonic Research (WAFER) similar to and building on AWS’s Well Architected Framework (WAF) for all early stage product and operational GenAI PoC’s across the organization.

• Oversee ML/AI related Kubernetes (k8s) cluster management and provide expertise on alternative ML/AI workflow orchestration optionssuch as Argovs Kubeflow, and ML/AI data pipeline creation, management and governance with tools like Airflow.

• Employ AWS CDK (TypeScript), Projen, and Argo CD to automate infrastructure deployment and management.

• Help set the strategy and manage the tactical balance between framework and platform experimentation and democratization with standardization and centralized management and governance.

• Conduct cost-benefit analyses and formal processes forselection and utilization of foundation models, evaluating their architectures, performance, and costs.

• Work with multiple teams to ensure that the platform meets organizational needs and scales effectively.

Personal Attributes:

• Customer-obsessed and passionate about building products that solve real-world problems.

• Highly organized and detail-oriented, with the ability to manage multiple initiatives and deadlines.

• Collaborative and inclusive, fostering a positive team culture where creativity and innovation thrive.

- Minimum of 10 years in cloud infrastructure design and management roles.

Previous experience as an AWS DevOps or Cloud Architect with a focus on ML/AI.

- Demonstrated ability to manage large-scale, complex projects across an organization.

- Proven experience in conducting performance and cost analyses of AWS infrastructure and ML/AI models.

- HS Diploma and 8 years of experience in Engineering/IT solutions OR BA/BS Degree and 6 years of experience or equivalent capabilities.