About Cognizant Corporate
Cognizant Corporate is a global community united by a shared purpose: to make a meaningful impact. We are committed to excellence and driven by outcomes that matter. Collaboration is at the heart of how we work, and our forward-thinking mindset fuels continuous learning, innovation, and growth.
At Cognizant, careers transcend titles. We empower our people to think strategically, inspire others, and lead with purpose – always guided by our core values. Join us in shaping future of business.
About the role
As a Senior Solution Architect , you will drive impactful contributions and focus on outcomes. You will be a key member of the enterprise AI solutions and architecture team , collaborating with AI Tools Enablement Program Lead, business unit technology teams, data engineering, cloud infrastructure, security, and enterprise architecture functions .
You will have the autonomy to lead, innovate, and improve workflows while upholding our commitment to quality and continuous learning. Operating at a Senior Manager level, this role sits at the intersection of technical architecture and enterprise AI strategy — shaping how AI tools are integrated into business systems, data ecosystems, and developer workflows at scale.
The incumbent will be the technical authority for AI solution design across the organization — owning architecture blueprints, integration patterns, and the technical standards that govern how AI tools connect with enterprise applications, data platforms, and cloud infrastructure. While the Program Lead drives adoption and change management, the Senior Solution Architect ensures the technical foundation is robust, scalable, secure, and fit for enterprise deployment.
As a team of self-starters, you can work with impact with our vibrant people and culture all while enjoying unmatched learning opportunities.
In this role, you will:
Architecture ownership - Define and own end-to-end AI solution architecture for enterprise AI tools programs — spanning Generative AI platforms, LLM integrations, AI-powered applications, and agentic workflow systems
Integration architecture - Design scalable, secure, and cloud-native integration architectures that connect AI tools (e.g., Azure OpenAI, Anthropic Claude, Google Gemini, GitHub Copilot) with enterprise systems including ERP, CRM, ITSM, data platforms, and internal APIs
Blueprints & standards - Develop reusable architecture blueprints, integration patterns, reference architectures, and technical standards for AI tool deployment across business units
Design leadership - Lead technical design sessions, architecture reviews, and solution validation workshops with engineering teams, platform owners, and enterprise architects
AI tools authority - Serve as the organization's deep technical expert on the AI tools landscape — maintaining current knowledge of LLM platforms, vector databases, RAG architectures, fine-tuning pipelines, embedding models, AI APIs, and agentic frameworks
Tool evaluation - Evaluate AI tools and platforms for enterprise readiness across dimensions of scalability, security posture, integration flexibility, data residency, cost efficiency, and vendor support
PoC architecture - Define technical selection criteria and conduct proof-of-concept architecture designs to validate AI tools before enterprise rollout
Ecosystem awareness - Maintain awareness of the evolving AI platform ecosystem including emerging foundation models, open-source LLM options, and AI infrastructure innovations relevant to enterprise deployment
Data integration - Design integration architectures that connect AI tools with enterprise data sources — data lakes, data warehouses, knowledge bases, document repositories, and real-time streaming platforms — using patterns such as RAG, semantic search, and vector store integration
API & data governance - Establish and enforce API design standards, data contract frameworks, and integration governance practices for AI tool connectivity across enterprise systems
Data pipeline design - Collaborate with data engineering, MLOps, and platform teams to design pipelines for data ingestion, preprocessing, embedding generation, and model output handling
Compliance by design - Ensure architecture decisions support enterprise requirements for data privacy, access control, audit logging, and compliance with regulations such as GDPR and DPDP
AI security architecture - Define and govern the technical standards for secure AI tool deployment — including prompt injection safeguards, data masking, PII handling, model access controls, and output filtering frameworks
Risk & compliance - Work with Information Security, Risk, and Compliance teams to conduct architecture risk assessments for new AI tools and integration patterns
Enterprise guardrails - Establish guardrails and enterprise control frameworks that enable AI tool adoption while managing model behavior risks, data leakage, and vendor dependency
Responsible AI architecture - Drive responsible AI architecture principles — ensuring explainability, fairness, and auditability are considered at the design stage of AI solutions
Technical advisory - Act as the primary technical advisor to the AI Tools Enablement Program Lead, providing architecture guidance, feasibility assessments, and technical risk inputs to program decisions
Executive engagement - Engage with C-suite and senior business leaders to present AI solution architectures, articulate trade-offs, and align technology decisions to business strategy
Client & pre-sales support - Support client-facing engagements, RFP responses, and pre-sales activities by authoring technical solution proposals and architecture narratives for AI tools implementations
Technical mentorship - Mentor and guide junior architects and engineers, establishing a culture of architectural excellence and continuous learning across the AI solutions practice
Delivery governance - Provide hands-on architecture oversight during the delivery of AI solutions — reviewing implementation artifacts, resolving technical escalations, and ensuring adherence to architecture standards
Cross-functional delivery - Collaborate closely with cloud engineering, DevOps, MLOps, and platform teams to translate architecture designs into deployment-ready technical specifications
NFR ownership - Define non-functional requirements (performance, availability, latency, scalability) for AI tool integrations and validate that delivered solutions meet these benchmarks
Embrace our vibrant culture by striving for excellence, focusing on meaningful outcomes, and collaborating effectively. Take ownership, build relationships, and focus on personal growth to drive business strategy and foster an inclusive culture, creating unmatched career opportunities and impactful work.
What you must have to be considered
Bachelor's degree or higher in Computer Science, Software Engineering, Information Technology, or a related technical discipline; advanced degree preferred
12+ years of experience in Solution Architecture, Enterprise Architecture, or Senior Technical roles — with a strong track record of designing and delivering complex, large-scale technology solutions
4–6 years of hands-on experience in AI/ML or Generative AI solution design — including architecture, AI integrations, and AI platform deployments in enterprise environments
Demonstrated experience owning end-to-end solution architecture across at least one major enterprise AI or Generative AI program
Experience working in matrixed, global organizations with diverse engineering and business stakeholder groups
Deep knowledge of Generative AI platforms: Azure OpenAI Service, Anthropic Claude, Google Vertex AI / Gemini, and open-source LLMs (LLaMA, Mistral, Falcon)
Strong command of RAG (Retrieval Augmented Generation) architecture patterns, vector databases (Pinecone, Weaviate, Chroma, Azure AI Search, pgvector), embedding models, and semantic search design
Familiarity with AI coding assistants and developer AI tools: GitHub Copilot, Amazon Q Developer, Cursor, Tabnine — including enterprise deployment and policy configuration
Working knowledge of MLOps practices: model versioning, evaluation pipelines, deployment patterns (A/B, canary, shadow), and observability for AI applications
Technical Expertise – Integration & Cloud
Strong expertise in enterprise integration architecture: REST/GraphQL APIs, event-driven architecture (Kafka, Event Grid), microservices, and middleware platforms
Proficiency in at least one major cloud platform: Microsoft Azure (preferred), AWS, or GCP — including cloud-native AI/ML services and serverless architectures
Familiarity with identity and access management, zero-trust architecture, and enterprise security patterns as applied to AI workloads
These will help you succeed
Certifications (Preferred)
Microsoft Azure Solutions Architect Expert (AZ-305) or equivalent AWS / GCP architect certification
Microsoft Azure AI Engineer Associate (AI-102) or equivalent AI/ML platform certification
TOGAF, Zachman, or equivalent enterprise architecture framework certification
LangChain, Semantic Kernel, or relevant AI orchestration framework proficiency (formal or demonstrated)
A strong sense of ownership, desire to create meaningful outcomes, and passion for work that serves a greater good for customers, communities, or global challenges
The embodiment of Cognizant’s Values of: Work as One, Dare to Innovate, Raise the Bar, Do The right Thing, & Own It
Work model – Hybrid
We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a hybrid position requiring 2-3 days a week in a client or Cognizant. Regardless of your working arrangement, we are here to support a healthy work-life balance though our various wellbeing programs.
