Generative AI Platform Architect – Evinova

About the role

The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is a newly formed team will spearhead the design, creation, and operational excellence of our entire Generative AI, agentic systems, and LLM computational AWS ecosystem to catalyze and accelerate science led innovations.

Responsibilities

• Lead by example in creating high-performance, mission-focused and 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 Generative AI and agentic system 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 GenAI agent systems, workloads and processes.
• Architect and implement scalable AWS agentic GenAI cloud infrastructure in a multi-tenant SaaS environment.
• Deep understanding of challenges in deploying Generative AI applications and agents.
• Closely follow frontier developments in Generative AI and GenAI tooling, techniques, and technologies.
• Establish governance frameworks for agentic GenAI 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 GenAI-related Kubernetes (k8s) cluster management and provide expertise on alternative GenAI workflow orchestration options such as Argo vs Kubeflow vs ECS vs AgentCore, and GenAI data pipeline creation, management and governance with tools like Airflow or others.
• Employ tools like 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 for selection 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.

Essential Skills/Experience

• HS Diploma and 8 years of experience in Engineering/IT solutions OR BA/BS Degree and 6 years of experience or equivalent capabilities.
• Minimum of 10 years in cloud infrastructure design and management roles.
• Deep understanding of the Data Science Lifecycle (DSLC) and the ability to shepherd data science projects from inception to production within the platform architecture.
• Expert in Typescript, AWS CDK, Projen, and Argo CD and other Cloud Infrastructure CI/CD Tools
• Strong familiarity with Python
• Extensive experience in managing Kubernetes clusters and/or ECS for GenAI workflows.
• Solid understanding of foundation models and their applications in GenAI solutions.
• Strong background in AWS DevOps practices and cloud architecture.
• Deep knowledge of AWS services (Bedrock, Sagemaker, EC2, S3, RDS, Lambda, etc) and hands-on design and implementation of cloud systems (microservices architecture, API design, and database management (SQL/NoSQL)
• Experience with monitoring and optimizing cloud infrastructure for scalability and cost-efficiency.
• Able to collaborate effectively with engineering, design, product, science and security teams.
• Strong written and verbal communication skills for reporting and documentation.
• 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.