Enterprise Architect

Overview Proven experience driving AI strategy and implementation across multiple functions. Ability to engage CXO/VP-level stakeholders and influence decision-making. Strong program management skills to oversee AI roadmap, execution, governance. Expertise in end-to-end AI lifecycle (use case > data > model > deployment > monitoring > optimization). Understanding of ethical AI, regulatory compliance, data privacy norm. Experience in setting up AI Centers of Excellence (CoE). Certifications in AI/ML, cloud, or enterprise architecture. Ability to translate business challenges into AI-driven solutions. Strong communication, consulting, and change management capability. Strong knowledge of technologies and hands-on experience in building AI solutions using them: Generative AI (LLMs, RAG, Agentic AI); AI/ML Architecture & Solution Design; Python ecosystem (PyTorch, TensorFlow, FastAPI, LangChain, LangGraph, etc.); Cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI); Data Engineering & Vector Databases (FAISS, Pinecone, etc.); Expertise in MLOps / LLMOps / GenAI Ops (model versioning, deployment pipelines, monitoring); Production grade enterprise implementations including: strong understanding of enterprise data architecture (data pipelines, feature stores, knowledge grounding, vector DBs); experience integrating AI solutions into enterprise ecosystems (APIs, microservices, OSS/BSS/CRM systems); Knowledge of Responsible AI principles (bias mitigation, explainability, auditability, regulatory compliance); Strong awareness of AI security (prompt injection, data privacy, access controls). Good-to-Have: Experience in automation, AIOps, MLOps frameworks; Experience in cost optimization of AI workloads; Experience in industry-specific AI use cases. Responsibilities and Expectations from the Role AI Strategy, Advisory & Opportunity Identification: Engage with client stakeholders to understand business challenges and identify AI/GenAI opportunities; Shape AI strategy, use cases, and roadmap aligned to client objectives and deal strategy; Provide advisory on AI adoption, readiness, and transformation approach. Solution Architecture & Presales Solutioning: Design enterprise-grade AI/GenAI solution architectures tailored to client needs; Define integration patterns with existing enterprise ecosystems (APIs, microservices, OSS/BSS/CRM); Select appropriate AI platforms, tools, and accelerators aligned to solution requirements; Collaborate with CoEs, domain teams, and account teams to build comprehensive solution offerings. POC Development & Deal Acceleration: Develop rapid PoCs, prototypes, and demonstrations to validate AI solution feasibility; Showcase business value through working demos, use-case walkthroughs, and client presentations; Support RFP responses, solution bids, and technical discussions during deal cycles; Accelerate deal closure by de-risking solution approaches through demonstrable outcomes. AI Enablement & Handover to Account Teams: Create reusable solution blueprints, architecture documents, and implementation guidelines; Ensure structured knowledge transfer of AI solutions, POCs, and architecture to account/delivery teams; Support accounts during transition from presales to delivery by clarifying solution intent and design decisions; Enable continuity by ensuring accounts are equipped to implement and scale the solution. Collaboration & Ecosystem Engagement: Work closely with account teams, delivery units, AI CoEs, and domain SMEs; Orchestrate contributions across multiple teams to build end-to-end AI solutions; Facilitate alignment between business, engineering, and architecture stakeholders. Innovation, Reusability & Thought Leadership: Stay updated with evolving AI/GenAI technologies and assess applicability; Build reusable accelerators, frameworks, and solution assets; Conduct workshops, client demos, and knowledge-sharing sessions. AI Lifecycle Guidance (Advisory): Guide teams on AI lifecycle best practices (data, model, deployment, monitoring); Recommend MLOps / LLMOps approaches for scalable execution; Advise on performance, cost optimization, and scalability considerations. Responsible AI, Risk & Compliance Advisory: Provide guidance on Responsible AI principles, security, and compliance considerations; Identify risks and recommend mitigation approaches (data privacy, model risks, prompt security). Vendor & Platform Advisory: Evaluate AI platforms, tools, and partner ecosystems for suitability; Recommend build vs buy decisions aligned to client context. Qualifications Bachelor’s degree in information technology, Business or related field. MBA preferred. Experience in AI/GenAI Solution Architecture. Experience in Enterprise AI Strategy & Governance. Python & AI/ML Frameworks. Experience in Cloud AI Platforms. 5+ Years in AI/ML/GenAI. Salary Range: 151500-204900 a year. #J-18808-Ljbffr Tata Consultancy Services