Key Responsibilities

1. AI Consulting & Enterprise Engagements

● Lead and support enterprise AI consulting engagements — from discovery and use-case identification through PoC design, production deployment, and adoption.

● Engage with CXO, business, and technology stakeholders to define AI strategy, use-case portfolios, GenAI adoption roadmaps, and ROI frameworks.

● Conduct AI maturity diagnostics, architecture reviews, and readiness assessments across client organizations.

● Create reusable consulting assets: industry playbooks, reference architectures, evaluation rubrics, governance templates, and implementation toolkits.

2. PoC Development & Accelerator Building

● Design, prototype, and deliver enterprise-grade AI PoCs with clearly defined success metrics, evaluation criteria, and production-readiness checklists.

● Build and maintain a library of repeatable GenAI accelerators — RAG starter kits, agent templates, workflow copilots, and domain-specific demo packs.

● Apply best practices for hallucination mitigation, prompt engineering, guardrails, security review, and responsible AI governance.

● Contribute to the SAI PoC engine — defining standards for use-case selection, cost/latency/accuracy targets, and PoC-to-production transition playbooks.

3. AI Training Design & Delivery

● Design and deliver high-impact AI learning programs — from CXO executive immersions and functional tracks to technical practitioner bootcamps and role-based journeys.

● Develop hands-on lab guides, capstone projects, case studies, and real-world simulations that drive measurable learner outcomes.

● Translate complex AI/ML concepts into transparent, compelling narratives for diverse audiences — from the boardroom to the engineering floor.

● Contribute to certification standards, competency frameworks, assessment rubrics, and quality assurance processes for SAI training products.

4.



AI Platform & Labs Contribution

● Contribute to the SAI AI Enablement Platform — building sandbox environments, lab exercises, auto-evaluation harnesses, and prompt/agent pattern libraries.

● Align platform components with consulting accelerators and training modules to create a seamless practitioner experience.

● Participate in model evaluation, LLMOps tooling, and responsible AI toolchain design.

5. Thought Leadership & Community

● Publish POVs, white papers, blogs, and research on applied AI — establishing SAI India's intellectual authority in the market.

● Represent SAI at industry conferences, webinars, university events, and partner forums.

● Mentor junior AI practitioners, researchers, and interns within the SAI ecosystem.

What You Must Bring

A. Deep AI Expertise (Non-Negotiable)

● 8–15+ years of experience in AI/ML, with at least 4–6 years focused on GenAI, LLMs, and applied enterprise AI.

● Hands-on proficiency in: LLMs (GPT-4, Claude, Gemini, Llama, Mistral), RAG architectures, prompt engineering, AI agents, and multi-modal AI.

● Working knowledge of: LLMOps/MLOps, evaluation frameworks, guardrails, vector databases, orchestration frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).

● Experience with cloud AI platforms: Azure OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face.

B. Proven Build Record: PoCs + Production

● Personally led at least 3–5 enterprise AI PoCs with measurable outcomes — not just advisory, but hands-on design and delivery.





● Demonstrated experience taking AI solutions from prototype to production in real enterprise environments.

● Experience with at least one of the following at scale:

◦ RAG pipeline design and optimization

◦ AI agent architecture and deployment

◦ Enterprise GenAI platform components (guardrails, evaluation, model routing)

◦ AI skilling/training platform (labs, sandboxes, auto-eval, certification)

C. Consulting & Communication Strength

● Demonstrated track record of delivering AI transformation engagements — including stakeholder management, value articulation, and executive communication.

● Ability to run structured workshops, design sprints, and discovery sessions with business and technology leaders.

● Strong written communication — capable of producing compelling proposals, POVs, case studies, and client deliverables independently.

D. Training & Facilitation Excellence

● Experience designing and delivering both executive (CXO/management) and technical (developer/data scientist) AI programs.

● Ability to build structured learning journeys: outcomes → curriculum → labs → assessments → certification.

● High-energy facilitation skills; comfortable leading intensive bootcamps, workshops, and executive roundtables.

E. Responsible AI & Enterprise Readiness

● Working knowledge of AI ethics, bias mitigation, explainability, data privacy (GDPR, DPDP Act), and enterprise AI governance.

● Understanding of AI risk frameworks and responsible deployment practices.

Education & Credentials

● Bachelor's degree required; Master's (M.Tech / MS) or MBA strongly preferred.

● Relevant AI/ML certifications from Google, AWS, Microsoft, DeepLearning.AI, or equivalent.

● Applied AI leadership track record carries more weight than pedigree — show us what you've built.

📌 AI Strategy Consultant (Mumbai)
🏢 Regenesys Corporate Education, India
📍 Mumbai