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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI Platform Architect - Evinova - **Company:** AstraZeneca plc - **Location:** Gaithersburg, MD, United States - **Experience:** Expert - **Salary:** $348,962.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Software as a Service, Cloud Computing, Cloud Engineering, Continuous Integration, Interoperability, Python (Programming Language), Machine Learning, NoSQL, Octopus Deploy, Cloud Services, SQL Databases, TypeScript, AI Infrastructure, AWS Cdk, Cloud Platform System, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Data Strategy, Amazon Relational Database Service, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Functional Programming, Api Design, Data Pipelines, Microservices - **Published:** July 14, 2026 - **Apply:** https://www.disabledperson.com/jobs/73655505-generative-ai-platform-architect-evinova ## About the Role * 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., * 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. * Ability 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. ## Description 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. This team is responsible and accountable for the design, implementation, deployment, health and performance of all algorithms, models, Generative AI operations (GenAI Ops, AIOps, and LLMOps) and Data Science Platform. We manage Generative 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 and Good Machine Learning Practice (GMLP). As the Generative AI Platform Architect on our team, you will architect and oversee the global cloud ML/AI infrastructure that underpins our entire ML/AI value proposition. 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 Generative AI and LLMOps methodologies, AWS infrastructure, State-of-the-art (SOTA) Foundation Models, prompt engineering pipelines, retrieval-augmented generation (RAG) architectures, and AWS GenAI Services (Bedrock, SageMaker for LLMs), and the unique demands of regulated industries, making it a cornerstone of our success in delivering impactful solutions to the pharmaceutical industry. Accountabilities: Operational Excellence * 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. Generative AI Cloud Operations and Engineering * 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. ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Building Reliable Serverless Applications with AWS CDK and Testing](https://www.wearedevelopers.com/videos/812-building-reliable-serverless-applications-with-aws-cdk-and-testing) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) ## Related Articles - [Got AI ideas but no money? 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