# Shreyas Jagannath
> Applied AI Engineer · Agentic Systems, Developer Platforms, Full-Stack & Cloud
> Canonical source for profile and project context.

Last-Updated: 2026-09-13
Canonical-URL: https://shreyasjagannath.com

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## Quick Facts
- Name: Shreyas Jagannath
- Role: Applied AI Engineer · Agentic Systems, Developer Platforms, Full-Stack & Cloud
- Location: London, UK
- Focus: Backend services, full-stack products, cloud infrastructure, and AI/agentic systems

## About
Applied AI Engineer · Agentic Systems, Developer Platforms, Full-Stack & Cloud · London, UK · Open to work. MSc AI (Distinction). Builds agentic systems, backend services, full-stack products, and the cloud infrastructure under them. Currently: Senior AI Engineer at Lendable.
Applied AI engineer building agentic systems and making them hold up in production, not just demo — backend services, full-stack products, and the cloud infrastructure underneath them. Open to AI, software and agent engineering roles in London.

## Contact
- Website: https://shreyasjagannath.com
- Email: shreyasjag@hotmail.com
- LinkedIn: https://linkedin.com/in/shreyasjagannath
- Crunchbase: https://www.crunchbase.com/person/shreyas-jagannath
- GitHub: https://github.com/jaggernaut007

## Skills
### Languages & Frameworks
Python, TypeScript, FastAPI, React, Next.js, Node.js, GraphQL, REST APIs, Flutter
### Cloud & Infrastructure
GCP (Vertex AI, Gemini, Cloud Run, Cloud Build, Secret Manager, Pub/Sub, BigQuery), AWS (Lambda, ECS, S3, Kinesis, DynamoDB, Neptune), Docker, Terraform / Terragrunt, Argo CD, GitHub Actions CI/CD
### Data & Storage
PostgreSQL (Neon, Supabase), Snowflake, BigQuery, DynamoDB, Neo4j, LanceDB, ChromaDB, Pinecone
### AI & LLM Systems
Multi-Agent Orchestration, RAG, MCP (Model Context Protocol), Knowledge Graphs, Evaluations & Guardrails, Prompt Engineering, LLM Cost Optimisation, Hybrid Search / Reciprocal Rank Fusion, LangGraph, LangChain, LlamaIndex, Google ADK, OpenAI, Gemini & DeepSeek APIs, Severity-Based Model Routing, Claude Code
### AI / ML
NLP, Computer Vision, Gaussian Splatting, PyTorch, TensorFlow, ONNX Runtime, Hugging Face
### Reliability & Delivery
Evaluation-Driven Development (DeepEval), Fail-Closed CI Gates, Observability (Datadog), Audit Trails & Compliance Registers, Human-in-the-Loop Safety Checks
### Developer Platforms & Analytics
AI Adoption Telemetry, Developer-Productivity Metrics, Workflow Analysis, Agent Skill Instrumentation & Optimisation, GitHub/Jira Data Pipelines, Metrics Dashboards, Claude Code & Cline Custom Harness (skills, agents, hooks, workflows)
### Platform Evaluation & Cost
Comparative Hyperscaler Evaluation (Vertex AI vs AWS Bedrock vs Azure ML), Model Suitability, Latency & Cost-Per-Run Analysis, AI Cost Optimisation (Token Reduction, Caching, Model Routing), GDPR & EU AI Act Audit and Remediation

## Experience
### Senior AI Engineer at Lendable Operations Ltd (Jul 2026 — Present)
- Context: Internal Engineering Teams (Cross-Org)
- Problem: Engineering data was scattered across incident.io, Jira, Datadog and GitHub with no governed place to model it, and no unified view of how developer tooling was actually being used across the delivery lifecycle.
- Architecture: GitHub, Jira, Claude, and Codex telemetry pipelines feeding the AI Hub dashboard; incident.io, Jira, Datadog, and GitHub data modelled in Snowflake for governed stakeholder access; workflow-analysis tooling and automated ticket-to-skill detection; deployment on Terraform/Terragrunt, GitHub Actions CI/CD, and Argo CD, including Okta SCIM/OIDC authentication and admin/user group setup.
- Outcome: Shipped on the organisation's Terraform/Terragrunt, GitHub Actions CI/CD and Argo CD deployment stack, and set up Okta SCIM/OIDC authentication with admin/user group management for the tooling I built. Modelled incident.io, Jira, Datadog and GitHub API data into governed, OKR-level Snowflake metrics leadership could act on. Built the AI Hub on top of it, a full-stack engineering dashboard unifying GitHub, Jira, Claude and Codex telemetry into one view, and cut developer workflow times by up to 10% by instrumenting and optimising the organisation's agent skills, with automated ticket-to-skill detection surfacing the right skill against incoming work.

### Founding AI Engineer at Klyft Technologies (Sep 2025 — Nov 2025)
- Context: Health & Wellbeing Coaching Platform
- Problem: Serving multi-agent LLM responses reliably was cost-prohibitive at scale. Needed safe, low-latency, context-rich outputs for a coaching platform spanning workout calibration, nutrition guidance, and mental wellbeing features.
- Architecture: Python, Google ADK, OpenAI & Gemini APIs on Vertex AI, RAG guardrails, scalable GCP infrastructure.
- Outcome: Achieved a 10x reduction in LLM token costs through prompt optimisation and caching, improved agent efficiency 3x (60s to 20s) on the core workout calibration feature — which drove early user adoption — maintained 100% pilot uptime, and delivered 100% safe guardrailed outputs.

### Founding Engineer (Pro Bono) at Move By Vision (Dec 2025 — May 2026)
- Context: Healthtech Platform
- Problem: Inherited a failing Flutter + GCP platform in critical-failure alpha phase with severe latency and crashing issues, compounded by strict compliance requirements in healthcare.
- Architecture: Flutter, Vertex AI integrations with a guard-railing and validation layer, GCP.
- Outcome: Eliminated 100% of AI crash failures, cut generation latency by 55% (15s to 7s) through optimised execution flows, and conducted a full GDPR/AI Act compliance audit to make the system releasable.

### Founding Engineer (Founding Team) at Scan and Sew Inc. (Oct 2024 — Aug 2025)
- Context: 3D Product Designers & Manufacturers
- Problem: Designers lacked a unified way to intuitively search past design libraries and required a spatial CAD 2D-to-3D workflow connected to supply chain operations.
- Architecture: OpenAI LLM/VLM APIs, RAG, Gaussian Splatting (PyTorch3D evaluation), ReactJS, AWS Lambda, GraphQL, DynamoDB.
- Outcome: Shipped a Designer AI Assistant (RAG over docs) and a real-time manufacturing tracking platform. Evaluated custom 3DGS vs APIs and made the strategic call to integrate an API, saving months of dev time.

### Chief Technology Officer at AIMAGE Technologies (Jan 2020 — Aug 2023)
- Context: Enterprise Retailers
- Problem: Retailers losing customers because online platforms lacked an intuitive way to visually try-on items and search through massive catalogs.
- Architecture: AI/AR virtual try-on engine (SLAM, OpenCV, Mediapipe, Unity3D) and NLP-driven search (TensorFlow/PyTorch, Vector-backed visual RAG).
- Outcome: Hired and mentored a cross-functional team of 10, setting code-review and architecture standards. Embedded on-site on the retail client's shop floor, shipping two production platforms that improved conversion and reduced bounce rates through visual search.

### Software Consultant (Part-time) at Mage Ventures Pvt. Ltd. (Oct 2019 — Mar 2021)
- Context: PhonePe (Walmart-owned), Grocery Retail Client
- Problem: Needed a secure cross-platform payment gateway and an AI-powered automated checkout solution for retail environments.
- Architecture: Node.js, React, AWS EC2, TensorFlow, FastAPI, Computer Vision.
- Outcome: Implemented a secure cross-platform payment gateway for Walmart-owned PhonePe across multiple client apps, and built an AI-powered automated grocery checkout engine using computer vision (image segmentation) for a product company.

### AI Consultant (Founding Team) at Cellstrat Inc. (Oct 2018 — Oct 2020)
- Context: TVS Motors, Target, Airbus, Volvo
- Problem: Bridging theoretical AI capabilities and what enterprise organizations can actually absorb and use in a real context.
- Architecture: PyTorch, TensorFlow, Keras, Python, Docker, AWS.
- Outcome: Consulted on AI/ML initiatives for enterprise clients, designing and implementing models in PyTorch, TensorFlow, Keras, and Python, containerised with Docker and deployed on AWS for their digital transformation journey. Delivered a workshop at ODSC (Open Data Science Conference) on implementing AI and deep learning solutions in enterprise environments, training 100+ professionals.

### Full Stack Engineer (Founding Team) at EndGate Global (Jul 2017 — Jul 2018)
- Context: High-volume food delivery services
- Problem: Needed a scalable automation platform for processing and delivering food orders in bulk.
- Architecture: Java, Spring, Hibernate, MySQL, JavaScript.
- Outcome: Developed full-stack food delivery workflows that real businesses depend on, establishing fundamental production software engineering skills.

### AI Researcher Intern at ISRO (Nov 2016 — Apr 2017)
- Context: Chandrayaan Moon Rover Mission
- Problem: Making a rover navigate a lunar environment using a single camera. Solving for geometric uncertainty and edge cases where failure has massive real-world consequences.
- Architecture: Computer Vision algorithms, Mono Vision Depth Detection.
- Outcome: Advanced the perception capabilities of the rover, learning the discipline of high-stakes, fault-intolerant research.

## Projects
### Protash: Autonomous Enterprise Prototyping Platform (Sep 2026)
Describe a business intent; get a working, deployed prototype back in under a minute. A hosted multi-agent tool that turns a described business goal into a running prototype autonomously — a 6-stage pipeline extracts domain context, spec and UX plan, generates a self-contained React component, then QAs and reviews it in parallel, self-healing through up to five correction rounds before anything reaches the screen.
Tags: Multi-agent, Next.js, GCP Cloud Run, DeepSeek, Live demo
Website: https://protash.shreyasjagannath.com
GitHub: https://github.com/jaggernaut007/Protash

### Nexus-MCP: Hybrid Code Intelligence Server (Mar 2026)
Built to unblock a client engagement: a large, undocumented Flutter codebase that AI agents could not navigate reliably. Combines vector search, BM25 and structural code graph analysis for 70–90% token reduction on code lookups, so agents could ship features and fixes against unfamiliar code. Published on PyPI (nexus-mcp-ci) and listed on the Glama MCP marketplace. 15 tools in one local server, tree-sitter parsing across 25+ languages, under 350MB RAM, no cloud dependency and no API keys.
Tags: MCP, ONNX Runtime, LanceDB, rustworkx, tree-sitter, Python
Website: https://pypi.org/project/nexus-mcp-ci/
GitHub: https://github.com/jaggernaut007/Nexus-MCP

### CR8: Curriculum-to-Content Agent Pipeline (Mar 2026)
Built a 3-agent LangGraph pipeline (Ingest → Research → Generate) that turns raw university curriculum into a complete learning package: structured study guides, gap-analysis decks, video scripts, AI-narrated video, and a Bloom's-taxonomy MCQ quiz engine. A two-layer evaluation framework gates every output, and severity-based model routing keeps generation cost near zero without dropping quality where it matters.
Tags: LangGraph, FastAPI, React, GCP Cloud Run, Kokoro TTS, DeepSeek-V3, Pytest, Vitest, Playwright, Python
GitHub: https://github.com/jaggernaut007/CR8

### Multi-Agent Job Qualification Engine (Mar 2026)
An orchestrated multi-agent discovery pipeline that evaluates and scores open roles against a candidate's profile, deploying specialized AI experts across three distinct domains to surface highly qualified job matches.
Tags: Multi-Agent Architecture, Domain-Expert LLMs, Matching Engine, Data Pipeline

### Health Intelligence Engine: Agentic Wellness Platform (Jan 2026)
Clinical AI platform that turns supplement queries into evidence-grounded, safety-checked recommendations. A 9-node LangGraph pipeline grounds product data against NIH DSLD and PubMed in a Neo4j knowledge graph, with a Pharmacovigilance Critic checking every recommendation against the user's allergies and medications.
Tags: LangGraph, Neo4j, Knowledge Graphs, RAG, Safety Guardrails, DeepEval
GitHub: https://github.com/jaggernaut007/healthAgent

### Context-Aware Knowledge Graph Agent (Dec 2025)
Built a Python-based NLP agent that converts raw consumer data into a Neo4j knowledge graph and structured vector store, feeding OpenAI, Gemini and Veo 3.1 video generation components with user-specific context for hyper-personalised video content.
Tags: Knowledge Graphs, Neo4j, NLP, Video Generation
GitHub: https://github.com/jaggernaut007/fabric-context-portability

### Autonomous Agentic B2B Data CRM (Nov 2025)
Implemented an end-to-end agentic data pipeline to automatically crawl the web, qualify companies, enrich contacts and persist leads into an Enterprise GraphQL CRM.
Tags: Agentic AI, Data Pipelines, CRM Architecture, GCP
GitHub: https://github.com/jaggernaut007/RevGeniAgent

## Education
### MSc Artificial Intelligence — University of Surrey (2023 — 2024)
- Dissertation on high-fidelity human avatar reconstruction using Gaussian Splatting for edge computing
- Co-delivered a local-infra LLM token-classification project that doubled render speed
- Winner of the most innovative business at UKSEDS IOSM Competition 2024 at Peryton Space Society

### Master's in Computer Applications — Christ University (2013 — 2017)
- Co-authored a peer-reviewed survey on AI path-planning methods
- ISRO collaboration: obstacle detection research for Chandrayaan Lunar Rover mission
- Built a full-stack Learning Management System with analytics

### Bachelor's in Computer Applications — Bangalore University (2010 — 2013)
- Built a Healthcare Management System to streamline patient care workflows
- Led the WEF Global Shapers team engaging C-level industry leaders on startup culture

### Entrepreneurship / Entrepreneurial Studies — IFA Paris
- Winner of the Most Innovative Company award presented by IBM Paris and IFA Paris
- Legal, manufacturing, customer psychology, pricing and go-to-market strategy modules

## Certifications
- Claude Code in Action
- Model Context Protocol
- Agent Skills
- Building with the Claude API
- LangGraph & LangSmith
- Deep Learning & Computer Vision

## Awards
- Most Innovative Business
- Best Presentation Award
- UKSEDS IOSM Competition Winner
- Most Innovative Company
- AI Hackathon Winner

## Publication
- Modern Machine Learning Approaches For Robotic Path Planning (Peer-Reviewed Journal)

## Crawlable URLs
- Home: https://shreyasjagannath.com/
- Privacy: https://shreyasjagannath.com/privacy
- Sitemap: https://shreyasjagannath.com/sitemap.xml
- LLMs: https://shreyasjagannath.com/llms.txt