Enterprise Architect

Location : Chennai/Hyderabad/Bangalore/Kolkata.

Required Skills & Experience

• 10+ years in IT & architecture, with clear progression from hands-on engineering (Java/.NET preferred) into enterprise architecture leadership.
• 5+ years in AI/ML, with 1+ year in Generative & Agentic AI (LLMs, SLMs, multi-agent orchestration).
• Model Training Expertise: Fine-tuning (SFT, RLHF, LoRA/QLoRA), RAG, evaluation pipelines.
• Hyperscaler Certifications: Architect-level expertise in at least two (AWS, Azure, GCP).
• MLOps/LLMOps Mastery: CI/CD pipelines, monitoring, observability, compliance, AI cost governance.
• CxO Advisory: Proven experience leading executive AI strategy discussions with Fortune 500 clients.
• Governance Expertise: Familiar with AI RMF (NIST), EU AI Act, Responsible AI practices.
• People Leadership: Experience mentoring teams and building cross-functional AI capability practices.
• Ecosystem Influence: Track record shaping AI initiatives through partner alliances (hyperscalers, startups, ISVs).

Good to have.

• Publications, patents, or open-source contributions in Agentic AI, LLMOps, or model compression/distillation.
• Recognized thought leadership (conferences, panels, media interviews).
• Industry domain depth in at least one vertical: Banking & Payments, Consumer Goods, Energy & Utilities, Healthcare, Insurance, Life Science, Logistics, Manufacturing, Media, Retail, Telecom, Travel & Hospitality
• Experience with AgentOps, model evaluation frameworks, and AI observability tooling.
• Functional Competencies
• Business Acumen: Identify and execute high-value AI transformations across industries.
• Consultative Mindset: Convert PoCs/PoVs into long-term transformation programs.
• Practice Builder: Mentor, hire, and scale AI architecture talent pools.
• Ecosystem Shaper: Drive co-innovation with hyperscalers, startups, and ISVs.
• Pragmatic Execution: Balance build-vs-buy, deliver under pressure, and navigate enterprise politics.
• Translation & Storytelling: Distill complex AI concepts into clear executive-ready narratives

Core Responsibilities

• Architect & Implement Enterprise AI Ecosystems:Design and implement modular, scalable AI platforms that integrate Large Language Models (LLMs), Small Language Models (SLMs), and multi-agent orchestration. Ensure architectures are cloud-native, secure, and industry-compliant.
• Drive AI Strategy:Serve as the anchor architect for major client accounts. Lead C-level discussions to define AI roadmaps that link technology to measurable business outcomes. Influence long-term investment strategies.
• Industry-Specific AI Solutions:Envision, Design and Deliver verticalized AI use cases across one or more industries, including Banking & Financial Services, Consumer Goods, Energy & Utilities, Healthcare, Insurance, Life Sciences, Logistics, Manufacturing, Media, Retail, Telecom, and Travel & Hospitality.
• Develop, Train & Optimize Models:Lead end-to-end model lifecycle: data preparation, training, fine-tuning, deployment, monitoring, and continuous improvement. Balance LLM vs. SLM adoption for cost, latency, and performance optimization.
• MLOps & LLMOps Leadership:Establish enterprise standards for MLOps/LLMOps pipelines (CI/CD, monitoring, observability, drift detection, compliance). Build agent lifecycle governance frameworks for scalable, auditable AI systems.
• People Leadership & Practice Building:Mentor and grow a community of architects and engineers. Build capability squads in GenAI/Agentic AI. Shape the company's AI practice, including skills development, hiring strategy, and succession planning.
• Ecosystem & Partner Engagement :Build and leverage strategic partnerships with hyperscalers (AWS, Azure, GCP), ISVs, and AI startups. Co-develop joint offerings, influence partner roadmaps, and co-sell AI solutions into strategic accounts.
• Innovation & Thought Leadership:Publish white papers, frameworks, and patents. Lead executive roundtables and contribute to the Tech Radar, ensuring the firm shapes the AI conversation in the market.