Enterprise Architect

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 struct ured 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

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 below 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: o Strong understanding of enterprise data architecture (data pipelines, feature stores, knowledge grounding, vector DBs) o 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, Bachelor's degree in information technology, Business or related field. MBA's can also be preferred Experience in AI/GenAI Solution Architecture Enterprise AI Strategy & Governance Python & AI/ML Frameworks Experience in Cloud AI Platforms 5+ Years in AI/ML/GenAI