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<div>The Cognizant Automation practice delivers enterprise-grade AI, Machine Learning, GenAI, Agentic AI, Smart Data Intake, and Intelligent Automation solutions across mission-critical business and IT processes. We are seeking a highly technical AI Architect for Automation Delivery to drive the design, engineering, and implementation of scalable AI automation programs for a North America-based client, with a strong focus on Banking and Financial Services (BFS) environments.<br/><br/>This role is deeply execution-oriented and requires strong architectural judgment, hands-on delivery leadership, and the ability to translate business needs into robust, production-ready AI systems. The Engagement Lead will own the technical roadmap, solution architecture, delivery governance, and operationalization of AI and automation solutions at scale, ensuring alignment with BFS regulatory, risk, and operational requirements.<br/><br/><strong>Key Responsibilities:</strong><br/><br/><strong>AI-Led Automation Architecture</strong><br/>• Lead the end-to-end architecture of AI/ML/GenAI/Agentic AI solutions-including model selection, data pipelines, orchestration layers, integration patterns, and deployment architecture-tailored for BFS use cases such as KYC/AML, fraud detection, credit decisioning, servicing, and regulatory reporting<br/>• Define reference architectures, reusable frameworks, and engineering standards for automation and AI workloads across financial operations<br/>• Architect solutions using cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex), IPA platforms (UiPath, Power Platform), and custom Python-based pipelines, ensuring compatibility with core banking systems, payment platforms, and risk engines<br/>• Conduct technical feasibility assessments, including data availability, model readiness, integration constraints, and infrastructure requirements within regulated BFS environments<br/>• Ensure solutions meet enterprise standards for security, scalability, observability, compliance, responsible AI, and BFS-specific model risk management expectations<br/>• Own the technical delivery lifecycle: requirements, solution design, development oversight, testing, deployment, and hypercare<br/>• Guide engineering teams on model training, prompt engineering, RAG pipelines, vector databases, orchestration frameworks, and automation workflows supporting high-volume financial processes<br/>• Oversee creation of APIs, microservices, connectors, and integration layers to embed AI into enterprise systems such as core banking, CRM, and data platforms<br/>• Implement CI/CD pipelines, MLOps practices, and automation deployment frameworks aligned with BFS governance and auditability needs<br/>• Drive performance tuning, model evaluation, monitoring, and continuous improvement of deployed AI systems<br/>• Establish AI governance and AI strategy including model lifecycle management, versioning, auditability, and risk controls consistent with BFS regulatory expectations<br/>• Serve as the primary technical advisor to client architects, product owners, and engineering leaders, supporting adoption and operationalization of AI solutions across financial operations<br/>• Lead AI programs, driving alignment between business stakeholders, technical teams, and delivery partners, with experience navigating BFS risk, compliance, and operational constraints<br/><br/><strong>Required Skills & Qualifications</strong><br/><br/><strong>AI/ML/GenAI Technical Expertise</strong> • Strong practitioner experience designing and implementing AI/ML pipelines, GenAI solutions, RAG architectures, and agent-based systems • Hands-on experience with cloud AI platforms: o Azure AI / Azure OpenAI o AWS AI/ML stack o GCP Vertex AI • Experience with vector databases (Pinecone, FAISS, Chroma, Redis), embeddings, prompt engineering, and LLM orchestration frameworks • Proficiency in Python, API development, microservices, and automation frameworks<br/><br/><strong>Candidate Background</strong> The ideal candidate brings a strong development and technical background, with hands-on experience in modern engineering stacks such as Python, Java, .NET, and related frameworks. A deep understanding of scalable architecture and clean coding practices is essential, along with experience delivering AI or automation solutions within regulated financial institutions.<br/><br/><strong>Automation & Integration Experience</strong> • Strong understanding of workflow orchestration, event-driven architectures, and enterprise integration patterns • Experience integrating AI with core systems (policy admin, claims, CRM, data lakes, APIs), including BFS platforms such as core banking, payments, lending, and risk systems<br/><br/><strong>Architecture & Delivery Leadership</strong> • Proven ability to lead large-scale AI automation delivery programs with complex technical dependencies • Strong background in MLOps, DevOps, CI/CD, model monitoring, and production deployment • Experience conducting architecture reviews, threat modeling, and performance optimization • Ability to create technical roadmaps, solution blueprints, and engineering playbooks<br/><br/><strong>Cognizant will only consider applicants for this position who are legally authorized to work in the United States without requiring company sponsorship now or at any time in the future.</strong><br/><br/> <br/> <br/>Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.<br/> <br/>Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.<br/> </div>
