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Senior Data Scientist / AI Product Builder

I design and ship AI systems that survive real production constraints.

From agentic orchestration to deployment and adoption, I turn research-heavy AI work into products teams can trust, operate, and scale.

Currently

Senior Associate in Data Science at Publicis Sapient, working across AI product strategy, ML systems, and production delivery.

Base
Hyderabad, India
Working Across
Agentic systems, recommendation engines, platform delivery, and operational reliability.
Best Fit
Ambiguous, cross-functional work where product shape and production discipline both matter.

Selected Work

Newest and selected work where product shape and system reliability had to move together.

The strongest work here is the work that had to survive product pressure, delivery constraints, and operational reality at the same time.

Personal memory layer2026New

A personal memory layer that captures what you encounter, threads the patterns, and gives it back when it matters.

Result

Built as a private-by-default memory system for people who consume a lot, remember too little, and want real recall in conversation.

Signals
  • Capture / thread / recall
  • Built for live conversational recall
  • Personal tool with product-grade framing
Outcomes
  • Turns scattered inputs into connected memory
  • Designed for curious people, not note-taking power users
  • Newest personal product build
Stack
  • Voice, text, image
  • Memory graph
  • Context retrieval
  • Private by default
Role

Founder / Product Design / AI Systems

Enterprise GenAI analytics platform2024–25

Insights IQ

Secure, agentic analytics for enterprise teams moving from experimentation to governed production.

Result

Launched a multi-tenant AI analytics product with orchestrated pipelines, rollout controls, and production guardrails.

Signals
  • AWS Marketplace launch
  • Multi-tenant security model
  • Rollback and observability paths
Outcomes
  • Cut analyst time-to-insight
  • Improved reliability under production load
  • Enabled secure enterprise adoption
Stack
  • LangGraph
  • FAISS
  • FastAPI
  • Kubernetes
  • gRPC
  • Ray / Celery
Role

Product Lead & Architect

Recommendation system modernization2024

AIBA GenAI Recommender

A self-service GenAI recommendation engine built to replace brittle legacy exploration flows.

Result

Reshaped a legacy recsys into a faster, more discoverable GenAI search and recommendation experience.

Signals
  • 70 percent latency reduction
  • Earlier-stage effort reduced by 20 percent
  • Improved engagement and accuracy
Outcomes
  • Reduced response time significantly
  • Simplified discovery for end users
  • Lifted quality without adding operational drag
Stack
  • Vector search
  • Hybrid search
  • RAG
  • Caching
  • Async
Role

Lead Developer (Data)

How I Work

The work gets better when the technical system and the operating model are designed together.

01

From prototype to operating model

I work across architecture, evaluation, latency, observability, rollout, and adoption so the system holds up after launch.

02

Product sense over demo polish

The target is not a clever model in isolation. It is a workflow people rely on under deadlines, cost limits, and operational pressure.

03

Clear tradeoffs, calmer systems

I prefer explicit guardrails, measurable quality, and steady delivery over complexity for its own sake.

Proof

A few signals that the work has shipped, landed, and been recognized.

Award
Best GenAI Case Study

Jun 2024

Award
Embracing the Future

Oct 2024

Award
Aspire Award Finalist

Mar 2024

Certification
GCP Professional ML Engineer

Apr 2024

Certification
Databricks GenAI Fundamentals

Apr 2024

Certification
IBM Data Science Certificate

Apr 2024

Elsewhere

Writing, utilities, and smaller builds that still earn a place here.