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JSJai Shankar

[ AI Engineer · Liverpool, United Kingdom ]

Jai Shankar
K Neelavathi

I design and productionise AI systems — diffusion pipelines, multi-agent frameworks and vision models that cut costs, win contracts and ship on deadline. 4+ years across Tata Elxsi and Flam; now at the University of Liverpool for an MSc in Data Science & AI.

Open to opportunities — full-time right to work in the UK

Jai Shankar K Neelavathi, AI Engineer
fig. 01 — Jai Shankar K Neelavathi● rec
4+
Years in AI engineering
7
Production AI projects
3
Industry awards
£42.5k
Contract won from one PoC

[ 03 / Selected work ]

Production AI, measured in outcomes

All projects →

Flam

Jun 2025 – Aug 2025

Hyper-Realistic Human Placement

Generative-AI framework that places a human subject into a different background and blends them naturally with the environment. Flux fine-tuned models combined with the IC-Light relighting model generate physically plausible lighting and shadows; image-processing passes smooth the borders. Delivered one week ahead of schedule.

≈35% lower GPU cost via inference and GPU management

FluxIC-LightPyTorchFastAPIDockerPython

Flam

Mar 2025 – May 2025

Wall Paint Visualiser

Wall detection and paint visualisation pipeline pairing YOLOv11 segmentation with a fine-tuned Stable Diffusion model. Renders the chosen paint colour onto walls while preserving light reflections on reflective surfaces. Deployed as a containerised inference service.

Proof of concept secured a client contract worth ≈£42,500

YOLOv11Stable DiffusionLitServeFastAPIDocker

Tata Elxsi

Jul 2024 – Feb 2025

AI Project Tracker & Visualiser

Multi-agent framework built on OpenAI GPT models that captures user inputs, updates project progress and generates client reports. NeMo Guardrails blocks prompt attacks; a Qdrant vector store powers retrieval (RAG). Later adapted for the IT department's ticketing system.

≈60% reduction in reporting and analysis time

OpenAI GPTNeMo GuardrailsLangChainQdrantRedisFastAPI

[ 05 / Contact ]

Have an AI problem worth solving?

From diffusion-model pipelines to agentic frameworks — let's talk about what intelligent systems can do for your product.