I build generative systems that ship — from 30B-parameter video models to language models small enough to run on your phone.
Selected work
Six systems, from a 30B multimodal model to edge inference in the field. Each one opens with the constraint that shaped it and the number that says whether it worked.
- 01
AURA
Video and audio from one forward pass
A ~30B-parameter foundation model that natively generates 2K video and synchronised stereo audio together, in a single forward pass, instead of stitching two models in post.
- PyTorch
- Diffusion Transformers
- Rectified Flow
- Latent Consistency Models
- Distributed training
- 30BParameters
- ≤8Sampling steps
- 2KVideo resolution
- 02
Sigil
Handshake deals into binding micro-contracts
An AI-native mobile app that turns a spoken agreement into a binding digital micro-contract, with the language model running on the device so the conversation never leaves the phone.
- On-device SLMs
- Speech processing
- Mobile
- Privacy-first architecture
- Subscription product
- 72.9MTarget market
- 0Cloud round-trips
- 03
Fine-tuning & RAG
Production LLM work across finance and developer tools
Two and a half years of shipping adaptation and retrieval systems for paying clients: fine-tuned LLMs for code generation and intent recognition, and RAG pipelines over financial documents.
- PyTorch
- GPT
- BERT
- FAISS
- Pinecone
- 30%Contextual accuracy
- 40%Manual effort
- 04
Radiography
AI-assisted radiographic analysis
A radiographic analysis system reaching 95% accuracy, built to cut the time a clinician spends before reaching a diagnosis — not to replace the clinician.
- PyTorch
- CNNs
- Transfer learning
- Medical imaging
- 95%Accuracy
- 40%Diagnosis time
- 05
Battlefield IoT
Edge ML threat detection for defence
Military IoT systems combining real-time analytics, edge computing and ML-based threat detection, deployed under DRDO and Army cybersecurity standards with a cross-functional team of ten-plus engineers.
- Edge AI
- Real-time analytics
- ML threat detection
- Embedded systems
- IS 15500
- 40%Battlefield intelligence
- 100%Compliance
- +10Engineers led
- 06
Observability
Telemetry, root cause and incident response
Real-time observability dashboards and telemetry analysis across production systems — where I learned that a system you cannot measure is a system you cannot improve.
- Azure Monitor
- Log Analytics
- Application Insights
- Power BI
- Power Apps
- 35%Incident response time
- 20%System uptime
- 25%Downtime
Case studies open with the constraints, not the stack.
Tokenizer, live
Type anything. This runs GPT-2's actual byte-level BPE in your browser — same merge table, same pre-tokenisation regex. Colour marks where words begin.
Load the tokenizer to see this text split into the units a model actually reads.
- Tokens
- —
- Characters
- 103
- Chars / token
- —
- Word starts
- —
Chars-per-token is why non-English text costs more to serve: the same sentence in a script GPT-2 saw little of fragments into far more tokens, and you pay per token. Try sample 2.
About
I am a generative AI engineer in Bengaluru. Five years in, split between shipping models people actually use and the systems work that makes them affordable to run.
I started in observability at Infosys — telemetry, dashboards, root cause. It is an unglamorous place to learn that a system you cannot measure is a system you cannot improve, and the habit followed me into machine learning: I build the evaluation before I build the model.
Since then it has been military IoT at DRDO, where inference had to happen at the edge because the uplink could not be trusted; two and a half years of freelance LLM and vision work, where a client will not accept a demo; and now a ~30B multimodal model that generates video and synchronised audio in a single forward pass.
The through-line is inference budgets. A model that only works at fifty sampling steps is a research result, not a product — getting it to eight is the part I find interesting. I am also building Sigil, which runs a small language model entirely on-device so a spoken agreement never leaves your phone. Constraints like that make the design decisions legible.
Bengaluru, India
Skills
- Daily
- Comfortable
- Exploring
Generative & multimodal
What I work on now
- Diffusion Transformers (DiT)
- Rectified flow matching
- Latent Consistency Models
- Step distillation / Reflow
- Cross-modal attention
- Audio-visual synchronisation
Language models
Adaptation, and making them small enough to ship
- Fine-tuning (GPT, BERT)
- Transfer learning
- Prompt engineering
- On-device SLMs
Retrieval
Opinionated: evaluate against the manual process
- RAG pipelines
- FAISS
- Pinecone
Vision & classical ML
Where a smaller model is the right answer
- CNNs
- Medical imaging
- Scikit-Learn
- XGBoost
Edge & IoT
From DRDO and IBM — inference where connectivity is not assumed
- Edge AI
- MQTT
- Raspberry Pi / Arduino / ESP32
- IBM Watson IoT
Platform & observability
Enough to own a system end to end, and to prove it improved
- Python
- PyTorch
- Azure Monitor / Log Analytics
- Application Insights
- Power BI
- AWS / Azure / IBM Cloud
Experience
- Apr 2026 — now
AI Research Engineer / ML Engineer
Stealth AI startup · Remote
Core contributor to AURA, a ~30B dual-branch flow-matching DiT generating 2K video and synchronised stereo audio in one forward pass. Cut sampling from 50 steps to ≤8 via step distillation.
- DiT
- Rectified flow
- LCM
- May 2026 — now
Founder
Sigil · India · Remote
Building an AI-native mobile app that turns oral agreements into binding digital micro-contracts, using on-device SLMs so voice never leaves the phone.
- On-device SLM
- Privacy-first
- Product
- Jan 2024 — now
ML Engineer & Gen AI Specialist
Fiverr · Freelance · Remote
Fine-tuned LLMs for code generation and intent recognition (+30% contextual accuracy) and built RAG pipelines over financial data (−40% manual effort) with FAISS and Pinecone.
- RAG
- Fine-tuning
- CNNs
- Dec 2023 — May 2026
Project Engineer
Centre for AI & Robotics, DRDO · India · Remote
Handpicked to design Military IoT systems with edge computing and ML threat detection, enhancing battlefield intelligence by 40%. Led 10+ engineers to a fielded deployment at 100% standards compliance.
- Edge AI
- Real-time analytics
- IS 15500
- Mar 2021 — Oct 2023
Senior System Engineer
Infosys · Mysore, India · Hybrid
Real-time observability across production systems: incident response down 35%, uptime up 20%, downtime down 25% through root cause analysis.
- Azure Monitor
- Power BI
- Telemetry
- Apr 2020 — Aug 2020
IoT Intern
IBM · Hyderabad, India · On-site
IoT solutions across AI, cloud and edge devices — sensor data into AWS, Azure and IBM Cloud, plus predictive maintenance models for real-time analytics.
- MQTT
- Edge AI
- Watson IoT