Preferred Technology Stack

AI platforms: Azure OpenAI, Azure AI Foundry, Copilot Studio, Microsoft Copilot ecosystem, OpenAI, Anthropic, Claude

Agentic AI: Semantic Kernel, LangGraph, LangChain, Microsoft agent frameworks, tool calling, orchestration frameworks, MCP-style integration patterns, API-based automation

RAG and knowledge: Azure AI Search, vector databases, knowledge graphs, embeddings, semantic search, document intelligence, enterprise content integration

Secure AI: AI guardrails, red-teaming approaches, logging, monitoring, evaluation, auditability, identity and access controls, DLP, privacy controls, and threat modeling practices

Data and analytics: Snowflake, Snowflake Cortex, data products, semantic layers, operational analytics, predictive analytics, and BI integration

Voice AI: ElevenLabs, real-time voice agents, contact center platforms, speech analytics, transcription, summarization, and conversational AI platforms

Strategic platforms: Palantir Foundry, Palantir AIP, ontology-based architecture, operational AI platforms, and enterprise integration layers

. Leadership Expectations

Acts as a senior leader in client conversations, not as a back-office technical expert

Creates confidence by connecting business value, technology feasibility, security, delivery, and commercial logic

Challenges clients and internal teams when use cases are weak, technology choices are premature, scope is unrealistic, or governance is insufficient





Builds repeatable AI consulting assets and raises the maturity of presales, delivery, and architecture teams

Stays ahead of the market and continuously translates emerging AI trends into practical services and offers

Balances strategic thinking with hands-on credibility and delivery pragmatism

. Success Metrics

Number and quality of AI opportunities discovered, shaped, and moved into qualified pipeline.

Win rate and quality of AI proposals, RFP responses, and solution offers.

Client feedback on advisory value, clarity, credibility, and strategic relevance

Percentage of AI engagements with explicit business outcomes, success metrics, architecture decisions, and governance model defined before delivery

Reduction in delivery ambiguity, rework, architecture escalations, and solution gaps between presales and implementation

Number of reusable assets created: reference architectures, demos, proposal modules, accelerators, playbooks, estimation models, and risk checklists

Measurable contribution to revenue, margin protection, delivery quality, and strategic AI capability building

Pipeline influenced, revenue influenced, strategic account growth, partner revenue, and contribution to qualified AI consulting opportunities

Proposal win rate, quality of commercial solutioning, margin protection, and ability to reduce delivery risk before contract signature

Reusable asset adoption across sales, presales, delivery, partner motions, and strategic accounts

📌 AI Solution Architect & Presales Lead (Pune)
🏢 Billennium
📍 Pune