Enterprise Architect

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:


Bachelors degree in information technology, Business or related field. MBAs 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


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