AI Engineer & ML Systems Builder
I build distributed ML infrastructure, agentic AI systems, and research-driven machine learning applications. Experienced with PyTorch Distributed, LLM orchestration, cloud platforms, and scalable software engineering for real-world AI workloads.
7
Engineering case studies
4,300+
Cities modeled
90 GB
Largest dataset processed
3
Deep learning projects
Selected Engineering Projects
Technical Focus
Distributed ML
- PyTorch DDP
- gRPC
- Distributed Training
- Scheduling
- Telemetry
- NCCL / Gloo
Agentic AI
- Multi-Agent Systems
- LLM Orchestration
- Ollama
- Tool Use
- Research Agents
- Consensus
ML Engineering
- PyTorch
- Python
- FastAPI
- Docker
- MLOps
- Model Serving
Research
- Computer Vision
- Medical AI
- Speech AI
- ML Systems
- IEEE Published
Selected Work
View all 7 projectsResearch & Publications
Speech Emotion Recognition
Real-time speech emotion recognition pipeline with enhanced data augmentation and lightweight CNN. Published at IEEE DECoN 2025.
Speech Emotion Recognition: A Human-Centric Framework with Enhanced Data Augmentation and Lightweight CNN
IEEE DECoN 2025
DOI: 10.1109/DECoN67170.2025.11448083Distributed AI Infrastructure
Scaling training and inference across heterogeneous compute clusters with minimal communication overhead.
Medical AI
Applying deep learning to medical imaging for segmentation, classification, and diagnostic assistance.
Multi-Agent Systems
Designing teams of specialized AI agents that communicate and collaborate on complex tasks.
Large Language Models
Efficient fine-tuning, alignment, and deployment of LLMs for production workloads.
ML Systems Engineering
Software engineering practices that improve reproducibility, scalability, and deployment of ML applications.
Experience
AI Research Intern
Coding Jr
- Defined product strategy for 3 core features by synthesizing competitive analysis of 5 major market players and client feedback into actionable requirements
- Increased client engagement by 15% through 10+ data-driven technical presentations translating complex model outputs into business value
- Analyzed data from 50+ potential clients to identify user pain points, collaborating cross-functionally with Engineering to prioritize the roadmap
Research Engineer
Independent Research
- Medical imaging segmentation: BraTS 3D U-Net with Attention mechanisms
- Speech emotion recognition: CNN-RNN pipeline on RAVDESS/CREMA-D datasets
- Weather prediction at scale: XGBoost models for 4,500+ cities using 90GB historical data
- Multi-agent systems: AgentForge and AI Research Orchestrator
- Published research paper in IEEE
- Focus on reproducible experimentation, software engineering practices, and practical ML pipelines
Optimus: Autonomous EnergyPlus Building Controller
Honeywell Campus Connect Hackathon
- Designed safety-constrained physical-AI controller for autonomous building operations using EnergyPlus simulation
- Integrated PyEnergyPlus API for live state observation and local LLM (Ollama/Qwen) with deterministic fallback for bounded supervisory planning
- Implemented hard safety validation: PMV/temperature guards for comfort buildings, ITE inlet temperature for data centers
- Built one-hour bounded cooling relaxation with automatic native schedule restoration
- Delivered Docker/Streamlit deployment, JSONL audit trail, and EnergyPlus SQL-derived KPI comparison reports
- GitHub: https://github.com/ShreyanshShakya/Optimus
Participant
OpenAI Build Week
- Explored Large Language Models, Multi-Agent Systems, AI Infrastructure, Rapid Prototyping, and AI Application Development
- Built modular AI architectures and collaborative reasoning workflows
- Developed functional AI prototypes under time constraints
Data Scientist Intern
Springer Capital
- Built maintainable ML pipelines for business applications
- Performed exploratory data analysis on structured datasets
- Designed preprocessing pipelines and developed predictive models
- Evaluated model performance using appropriate metrics
- Technologies: Python, SQL, Pandas, NumPy, Scikit-learn, Git
Technical Stack
Distributed ML
LLM Applications
Medical AI
Speech AI
Data Engineering
Cloud & Infra
Languages
Research & Software Engineering
By The Numbers
90 GB
Largest Dataset Processed
Handled end-to-end in training and evaluation pipelines
4,300+
Cities Modeled
City-specific weather forecasting across a decade of data
7
Engineering Case Studies
Distributed systems, agentic AI, ML, and research projects
1
IEEE Publication
Peer-reviewed research in speech emotion recognition