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