Machine Learning & Deep Learning Portfolio

🧠 2025: RL for Mathematical Reasoning (Gemma-3 Fine-tuning)

Signal: RLHF / Post-Training / Foundation Models

🔗 Model on Hugging Face

🎯 Motivation & Research Challenge

Mathematical reasoning in small language models (270M parameters) presents unique challenges: limited capacity for complex reasoning, tendency for verbose explanations, and difficulty in learning structured problem-solving approaches. The goal was to implement Group Relative Policy Optimization (GRPO) from scratch to enhance chain-of-thought reasoning while maintaining computational efficiency.

🔧 GRPO Implementation from Scratch

⚡ Advanced Reward Engineering

• Correctness Component:
  • SymPy-based mathematical equivalence checking (handles algebraic simplification)
  • Numerical tolerance for floating-point comparisons (±1e-6)
  • Multi-format answer parsing (fractions, decimals, expressions)
  • Binary reward: +1.0 for correct, 0.0 for incorrect
• Efficiency Penalty:
  • Token count penalty: -λ × (tokens in <think>...</think>)
  • Encourages concise reasoning while maintaining accuracy
  • Hyperparameter λ = 0.01 (tuned empirically)
• KL Regularization:
  • Prevents policy drift from SFT reference model
  • β = 0.02 (KL coefficient) for stable training
  • Computed per-token for fine-grained control

🔬 Memory & Compute Optimizations

📊 Training Results & Analysis

🔬 Technical Innovations

• GRPO from Scratch: Complete implementation of group relative policy optimization algorithm
• Mathematical Reward Design: Novel approach combining correctness, efficiency, and stability
• Memory-Efficient Training: Techniques for training large models on constrained hardware
• Structured Reasoning: <think>...</think> format for interpretable mathematical reasoning

🏭 2023-Present: Production RAG System (ARGO DATA)

Signal: Production Scale / Latency Engineering / System Design

🎯 Motivation & Production Challenge

Building a production RAG system that serves `200+` concurrent users with sub-100ms latency requires solving complex challenges: real-time index updates, content change detection, embedding consistency, and fault tolerance. The system needed to be "self-healing" - automatically adapting to content changes without manual intervention.

🔧 Self-Healing Architecture Design

⚡ Production-Scale Optimizations

• Embedding Pipeline Optimization:
  • Batch Processing: Process 100+ documents simultaneously
  • GPU Acceleration: CUDA-optimized embedding generation
  • Caching Strategy: Redis-based embedding cache with TTL
  • Async Processing: Non-blocking embedding updates
• Vector Database Engineering:
  • Pinecone Optimization: Custom indexing strategy for 1M+ vectors
  • Sharding Strategy: Namespace-based data partitioning
  • Query Optimization: Metadata filtering for faster retrieval
  • Connection Pooling: Persistent connections for reduced latency

🔬 Latency Engineering Deep Dive

🏗️ Infrastructure & Reliability

📊 Production Performance Metrics

🔬 Technical Innovations

• Self-Healing Index: Automatic content change detection and re-indexing
• Latency Optimization: Multi-level caching and async processing
• Production Reliability: Circuit breakers, health checks, and auto-recovery
• Scalable Architecture: Horizontal scaling with load balancing

👁️ 2023-Present: ID Verification Vision System (ARGO DATA)

Signal: Computer Vision / Cloud Cost Optimization / Production ML

Challenges Solved

Cut vendor API costs by ~60% by replacing 3rd-party APIs with in-house PyTorch models deployed on Azure.

Technical Depth

• Architecture: "Hydra-Net" architecture (3 heads, 1 backbone) for multi-task learning
• Optimization: 50% memory usage reduction enabling efficient autoscaling
• Cost Engineering: Significant cost reduction while maintaining accuracy
• Cloud Deployment: Azure-based inference with auto-scaling capabilities
• Model Design: Multi-head architecture for document verification tasks

🛰️ 2022: Hyperspectral Super-Resolution (ISRO Research)

Signal: HPC / CUDA Optimization / Research Rigor

🔗 Research Publication

Challenges Solved

Accelerated training 30x on 1TB+ satellite datasets via custom CUDA-level optimizations for hyperspectral image processing.

Technical Depth

• Performance: 30x training acceleration through CUDA optimization
• Scale: Handling 1TB+ satellite datasets efficiently
• Architecture: SR-GAN pipeline for 4x resolution upscaling (20m → 5m)
• Infrastructure: Multi-GPU cluster optimization and distributed training
• Research Impact: Published research with ISRO collaboration

🚦 2022: TrafficSwarm (Multi-Agent Reinforcement Learning)

Signal: Reinforcement Learning / Multi-Agent Systems / Distributed AI

🔗 GitHub Repository

Challenges Solved

Solved decentralized coordination for traffic grid optimization using shared-context reinforcement learning.

Technical Depth

• Multi-Agent RL: Coordinated behavior across multiple autonomous agents
• Algorithm Development: Custom policy optimization algorithms using RLlib and PyTorch
• Distributed Systems: Decentralized coordination without central control
• Simulation: Complex traffic simulation environments with realistic constraints
• Performance: Improved traffic flow efficiency through learned coordination

🔬 Research & Innovation Highlights

Core ML/DL Expertise

• Foundation Models: RLHF, post-training optimization, and fine-tuning at scale
• Computer Vision: Multi-task learning, super-resolution, document processing
• Reinforcement Learning: Policy optimization, multi-agent systems, reward engineering
• Production ML: Latency optimization, cost engineering, scalable deployment

Technical Specializations

• Optimization: CUDA programming, memory optimization, distributed training
• Architecture Design: Multi-head networks, attention mechanisms, efficient inference
• MLOps: Production deployment, monitoring, auto-scaling, cost optimization
• Research: Academic collaboration, publication-quality research, novel algorithm development

Technology Stack

• Frameworks: PyTorch, Transformers, RLlib, LangChain
• Infrastructure: Azure ML, CUDA, Multi-GPU clusters, Docker
• Databases: Pinecone, Vector databases, Real-time indexing
• APIs: FastAPI, Production serving, Auto-scaling systems

📊 Impact Metrics

• Production Scale: `200+` concurrent users with sub-100ms latency
• Cost Optimization: 60% reduction in vendor API costs
• Performance: 30x training acceleration through optimization
• Research: Published work with government research organization (ISRO)
• Open Source: Multiple repositories with community adoption