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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