Enterprise Architect

Job description

• 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 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 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 Qualifications: 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 Salary Range: 151500-204900 a year #LI-MM6 </div