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B · THE TEAM

Engineering strength.
AI-native product development.

A capability dossier on the engineers, architects, and builders behind AI Linc's technology product practice — leadership, bench, and the operating standard that anchors every product we ship.

2
Principals at the helm
50+
Companies invested in
8+
Years at Microsoft
200+
Speaking engagements

Sandeep Volam

Chairman · Strategy & Corporate Relations

Chairs the AI Linc board and owns the company's strategic direction — enterprise partnerships, client relationships, governance, and long-term growth. A Harvard graduate with twenty-plus years of operating and investing experience, Sandeep has backed and built fifty-plus companies across technology and emerging industries.

50+
Companies invested in
20+
Years operating & investing
Harvard
University graduate
InvestorBoard ChairHarvard AlumCorporate Strategy

"Strategy is the work of choosing what not to do — and being the right partner to the institutions that matter."

Shubham Lal

Founder · Chief Architect

Eight-plus years at Microsoft across enterprise cloud, AI, and developer-tooling teams. Personally owns architecture review, technical hiring bar, and engineering-quality sign-off on every product AI Linc ships. Deep technical fluency across LLM architectures, MLOps, RAG, computer vision, AI security, Azure and multi-cloud, Kubernetes, and AI-assisted developer workflows.

8+
Years at Microsoft
32
Tech disciplines covered
200+
Speaking events · TEDx · JOSH Talks
Ex-MicrosoftTEDx SpeakerAI & Cloud ArchitectJOSH Talks

"Build the way industry actually builds — not the way textbooks describe it."

Three layers of engineering.

Beneath the principals, three execution layers govern engineering quality and delivery. Each layer is staffed by senior practitioners — active builders, not delivery middlemen.

I

Architecture Review Board

Senior architects from AI/ML, cloud, and security who own technical validation. Every new project goes through a two-stage system-design and security review before any code ships. No exceptions, no shortcuts.

II

Engineering Quality Council

Active builders who run our technical-vetting process — system design, code review, and a paid pilot project — for every new engineer joining the bench. Full authority to reject.

III

Delivery Operations

Engineering managers and program leads who own shipping cadence, code-review SLAs, deployment quality, and client communication on every engagement — fixed-bid, dedicated team, or staff augmentation.

Sourced from the top of the stack.

Every engineer on the bench is an active or recently-active builder shipping production code. No staff-aug intermediaries.

MAANG
Meta · Apple · Amazon · Netflix · Google
ML Engineers · SDE-II/III · Cloud Architects · Data Engineers · Security Engineers
5–12 yrs
Microsoft
Azure · M365 · Copilot · DevDiv
AI Researchers · Cloud Solutions Architects · Principal Engineers · Platform Engineers
6–15 yrs
Top Product Startups
Razorpay · Zerodha · CRED · Swiggy · Flipkart
Tech Leads · Senior SDEs · ML Engineers · Founding Engineers
4–10 yrs
Tier-1 Enterprises
Infosys · TCS · Wipro · Accenture · Deloitte
Solution Architects · Domain Specialists · Practice Leads
8–18 yrs
AI-Native Labs
Frontier model & applied-AI shops
GenAI Engineers · MLOps Specialists · LLM Researchers · Applied Scientists
3–9 yrs

Nine AI sub-disciplines.

Our AI engineering team is decomposed into nine specialist tracks — each populated by builders who do that work in production.

i.
Generative AI & LLMs

Frontier models, multi-modal, agent architectures.

ii.
MLOps & LLMOps

Model registries, A/B, drift detection, RAG observability.

iii.
RAG & Knowledge Systems

Chunking, embeddings, re-ranking, hybrid retrieval.

iv.
Classical ML & Data Science

BFSI risk, recommenders, forecasting, customer analytics.

v.
Computer Vision & Multi-Modal

Manufacturing QA, medical imaging, document AI, VLMs.

vi.
AI Security & Governance

Adversarial robustness, secure RAG, LLM red teaming, NIST AI RMF.

vii.
Applied AI for Business

Workflow automation, copilots, decision-support systems.

viii.
AI-Assisted Engineering

Copilot-style, Cursor-style, agentic coding at production scale.

ix.
Edge AI & On-Device

Quantization, pruning, ONNX, TensorRT, industrial AI.

Four ways to build with us.

Same vetted bench, optimised for different product shapes, risk profiles, and timelines. Squads confirmed in 5–10 days.

I

Fixed-Bid Project

Defined scope, fixed price, fixed timeline. RAG platforms, custom LLM integrations, AI copilots, MVPs.

8–16 weeks
II

Dedicated Squad

A full-time engineering pod — tech lead + senior engineers + specialist (ML / security / DevOps), embedded with the client team.

6–18 months
III

Staff Augmentation

Senior ICs deployed alongside client teams to fill specific capability gaps — AI architects, MLOps leads, security engineers.

Weeks to months
IV

Discovery & Architecture Sprint

Bounded engagement to scope an AI product, validate feasibility, and produce a buildable architecture, prototype, and cost model.

2–4 weeks
5–10 days to assemble squadAI-first engineering biasMAANG+ pedigree barGlobal client coverage