AIA - Principal Architect

AIA - Principal Architect Cognizant - 3.8 Philadelphia, PA Job Details Full-time 9 hours ago Qualifications AI models Content creation for technical audiences Continuous Delivery (CD) implementation Testing and evaluation Production systems Machine intelligence Model deployment Developing automated testing protocols Model training Cloud automation Machine learning (ML) fundamentals DevOps automation Unsupervised learning Model evaluation Project stakeholder communication Stakeholder management System performance monitoring Full Job Description Job Summary Serve as a principal architect designing advanced artificial intelligence and machine learning solutions on cloud infrastructure for a multinational organization using cloud native services and automation to deliver secure scalable and resilient platforms that create measurable value for customers and society while guiding teams through complex transformation initiatives in a hybrid work environment. Responsibilities Drive end to end architecture for artificial intelligence and machine learning solutions on cloud infrastructure that address complex business challenges and create measurable outcomes for customers and communities through reliable and scalable digital products. Define reference architectures and reusable design patterns for cloud native data pipelines and machine learning platforms that enable rapid experimentation robust model deployment and consistent governance across multiple product lines. Design secure multi account cloud environments using infrastructure as code practices with cloud template automation to ensure repeatable compliant and auditable provisioning of networking compute storage and data services. Collaborate with data scientists engineers and product partners to translate analytical use cases into pragmatic technical designs that balance innovation performance cost efficiency and operational simplicity. Guide the selection configuration and integration of cloud machine learning services including model training feature storage orchestration and monitoring to build resilient pipelines from data ingestion through model lifecycle management. Oversee non functional architecture concerns such as resilience reliability observability privacy and regulatory compliance by embedding these controls into blueprints guardrails and automated validation checks from the start. Conduct architecture reviews and technical deep dives for critical initiatives providing structured recommendations that reduce risk improve system quality and align with enterprise strategy and external regulatory expectations. Mentor engineering and data teams on architectural best practices for artificial intelligence workloads including model deployment strategies data partitioning approaches cost optimization techniques and automation driven operations. Partner with cybersecurity and risk stakeholders to design identity access and data protection models that safeguard sensitive information used by artificial intelligence solutions while preserving usability and analytical agility. Create clear architecture documentation including diagrams decision records and transition roadmaps that enable shared understanding across engineering operations product and senior stakeholder communities. Evaluate emerging technologies in artificial intelligence machine learning and cloud services by running targeted proofs of concept and providing objective guidance on adoption timing integration strategy and potential societal impact. Optimize platform performance and cost by analyzing usage telemetry tuning resource configurations and recommending architecture changes that improve efficiency without compromising resilience or customer experience. Support hybrid ways of working by enabling collaboration ready architectures standardized templates and automated environments that teams can use consistently regardless of physical location or time zone. Qualifications Demonstrate extensive experience delivering large scale solutions on major cloud platforms with strong focus on infrastructure as code automation cloud networking and secure multi account architectures. Bring deep practical expertise in designing and operating machine learning systems including data preparation feature management model training model deployment and ongoing performance monitoring in production contexts. Apply advanced knowledge of artificial intelligence concepts such as supervised learning unsupervised learning and model evaluation to frame realistic solution options and trade offs for diverse business domains. Show proficiency with cloud template authoring tools configuration strategies and modular design approaches that support reuse version control automated testing and continuous delivery of infrastructure resources. Exhibit strong ability to communicate complex architectural decisions through concise documentation and structured storytelling that enables technical and non technical stakeholders to make informed decisions. Display solid understanding of security data protection and responsible artificial intelligence principles ensuring that architectures follow regulatory expectations ethical guidelines and company policies. Certifications Required No.