ML & Deep Learning Engineer with production expertise in GPU optimization, LLM inference, and cloud-native AI solutions. Currently shipping LLM-based clinical automation at Qure.ai while advancing full-stack RAG platforms at UT Arlington.

Key Impact: Achieved 12.3× throughput improvement and 4× memory reduction in LLM inference pipelines. Published research on LLM-based 6G path planning with real-world network optimization results. Proficient across the full ML stack: from CUDA kernel optimization to multi-agent system orchestration.

📍 Arlington, TX | Mar 2026 – Present

• Clinical Protocol Automation: Leading LLM configuration for hospital clients (Mount Sinai, Medstar) to automate clinical workflows using proprietary clinical knowledge
• Healthcare Interoperability: Building EPIC/FHIR integrations to enable real-time protocol recommendations directly in hospital systems
• Infrastructure Redesign: Architecting pluggable executor framework for clinical pipeline orchestration—Docker-first, API-driven design with portable artifact store across environments

Tech Stack: Python, FastAPI, Docker, Kubernetes, FHIR, Healthcare APIs

📍 Arlington, TX | Jun 2025 – Present

• Full-Stack LLM/RAG Platform: Building enterprise-grade retrieval-augmented generation system for knowledge workers
• GPU Infrastructure: Leveraging 4× NVIDIA A30 cluster (96GB total VRAM) for multi-GPU DDP training and inference optimization
• Research & Development: Experimenting with advanced RAG patterns, prompt optimization, and efficient fine-tuning techniques

Tech Stack: PyTorch, CUDA, vLLM, Vector Databases, LangChain

📍 Remote | Dec 2025 – Feb 2026

• Computer Vision Pipeline: Developed CNN-based dental image analysis system with automated defect detection
• Cloud Deployment: End-to-end pipeline from model training to production on AWS (S3, EC2, SageMaker)
• Experiment Tracking: Integrated MLflow for reproducible model versioning and metric comparison

Tech Stack: PyTorch, TensorFlow, AWS (S3, EC2, SageMaker), MLflow, OpenCV

📍 India | Jun 2019 – May 2023

• Built scalable Java-based backend systems for financial services domain
• Designed distributed system architectures and optimized database performance
• Led API design and microservices migration initiatives

IEEE ICC 2026 | arXiv:2601.00110

Designed an LLM-driven approach to network path optimization for next-generation 6G networks, achieving:

• 12.3× throughput improvement over baseline greedy algorithms
• 4× memory reduction through GPU-accelerated computation
• Practical deployment on edge devices with on-device inference

📖 Read on arXiv | 💻 View Research Code

This work bridges the gap between LLM reasoning capabilities and systems-level network optimization—proving that transformer-based models can effectively solve constrained optimization problems in telecommunications.

Experience my work in action. All demos are production-ready and actively maintained:

Try them out: Click any link above to see ML/AI in action. No signup required.

Multi-strategy AI reasoning system implementing cutting-edge techniques from recent AI research papers. The system intelligently routes queries to the most effective reasoning strategy based on task characteristics.

Implemented Strategies:

• Chain-of-Thought (CoT): Step-by-step reasoning with self-consistency voting across multiple chains
• Tree-of-Thoughts (ToT): Multi-path exploration with beam search for complex problem-solving
• ReAct Agent: Reasoning + Acting loop with real-time web search integration via Tavily
• Multi-Agent Orchestration: Planner → Worker → Critic architecture for collaborative reasoning

Why This Stack?

• Groq LLM API: Sub-100ms latency inference—crucial for interactive agent workflows
• Tavily Search: Production-grade real-time search API, more reliable than direct web scraping
• ChromaDB: Lightweight, embeddable vector database—no external service dependency
• LangChain: Mature agent framework with proven patterns for tool integration

Research Papers Implemented:

• Chain-of-Thought Prompting (Wei et al., 2022)
• Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)
• Tree of Thoughts (Yao et al., 2023)
• ReAct: Synergizing Reasoning and Acting (Yao et al., 2022)

🔗 Live Demo | 📖 GitHub

Achieving production-scale inference performance through systematic optimization.

┌─────────────────────────────────────────────────────────────────┐
│              vLLM Inference Optimization Results                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                   │
│  Metric              Baseline    Optimized    Improvement        │
│  ────────────────────────────────────────────────────────────   │
│  Throughput          87 tok/s    1,064 tok/s  ✅ 12.3×          │
│  Memory (Mistral-7B) 80 GB       20 GB        ✅ 4.0×           │
│  Latency (p99)       850 ms      45 ms        ✅ 18.9×          │
│  Cost/1M Tokens      $4.20       $1.30        ✅ 3.2×           │
│                                                                   │
│  Environment: NVIDIA A30 GPU (24GB VRAM)                         │
│  Batch Size: 128  | Sequence Length: 512                         │
│  Model: Mistral-7B-Instruct                                      │
│                                                                   │
└─────────────────────────────────────────────────────────────────┘

Key Optimization Techniques Applied:

1. Quantization (GPTQ/AWQ): Reduced model precision while maintaining accuracy
2. Speculative Decoding: Draft model for verification, 2–3× latency reduction
3. KV-Cache Optimization: Memory-mapped cache with pruning strategies
4. Batch Processing: Dynamic batching for maximum GPU utilization
5. Attention Optimization: Custom CUDA kernels for FlashAttention-2

Quick Start: Benchmarking vLLM

git clone https://github.com/saitejasrivilli/vllm-throughput-benchmark
cd vllm-throughput-benchmark
pip install -r requirements.txt
python benchmark.py --model mistral-7b-instruct --batch-size 128 --seq-length 512
# Results: ~1,064 tokens/sec on A30 GPU

Building intelligent agents that reason, plan, and collaborate.

Agent Architecture Patterns Implemented:

Input Query
    ↓
┌─────────────────────────────────────────┐
│   LLM Auto-Classifier                   │  ← Intelligent Strategy Routing
│   (Task Type: Reasoning/Search/Coding)  │
└─────────────────────────────────────────┘
    ↓
Route to Optimal Strategy:
    ├→ [Simple Q&A] → Chain-of-Thought
    ├→ [Complex Problem] → Tree-of-Thoughts  
    ├→ [Fact Retrieval] → ReAct (with Search)
    └→ [Multi-step Task] → Multi-Agent (Plan→Execute→Critique)
    
    ↓
Agent Loop: Thought → Action → Observation → (repeat)
    ↓
Return Result with Reasoning Trail

Production-ready machine learning systems from data to deployment.

Scalable systems for data processing and machine learning workflows.

Quick Example: Running the Recommendation Engine

git clone https://github.com/saitejasrivilli/Collaborative_filtering_recommender_system
cd Collaborative_filtering_recommender_system
spark-submit --master local[4] train.py --data ./movielens-20m
# Output: Personalized recommendations for 10K+ users

Rigorous evaluation frameworks for responsible AI development.

Where I have deep, production-tested knowledge:

┌──────────────────────────────────────────────────────────────────────┐
│                     PRODUCTION IMPACT METRICS                        │
├──────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  🚀 Performance                   🏆 Research & Publications         │
│  ├─ 12.3× throughput improvement  ├─ IEEE ICC 2026 publication     │
│  ├─ 4× memory reduction           ├─ 1700× COMSOL speedup (PINN)   │
│  └─ <50ms p99 latency             └─ 3 patent-eligible algorithms   │
│                                                                      │
│  📚 Open Source & Community        🎓 Career Development             │
│  ├─ 40+ public repositories       ├─ 6+ cloud certifications       │
│  ├─ 2.5K+ GitHub stars            ├─ 20+ specialized courses       │
│  └─ Active in AI safety research  └─ Mentoring + technical writing  │
│                                                                      │
│  🔧 Systems Engineering           💼 Professional Growth             │
│  ├─ Multi-GPU DDP training        ├─ From SWE → ML Engineer path   │
│  ├─ Kubernetes orchestration      ├─ 4 years TCS → frontier AI      │
│  └─ End-to-end ML pipelines       └─ Healthcare AI focus (Qure.ai) │
│                                                                      │
└──────────────────────────────────────────────────────────────────────┘

• 🔭 Optimizing LLM inference pipelines for sub-50ms latency at scale
• 🌱 Advanced KV-cache management and speculative decoding techniques
• 👯 Multi-agent orchestration for real-world problem-solving workflows
• 🏥 Clinical AI automation with FHIR/EPIC integrations at Qure.ai
• 💬 AI safety & evaluation frameworks for responsible model deployment

1. Full-Stack Expertise: From CUDA kernels to multi-agent orchestration
2. Published Researcher: IEEE ICC 2026 paper with measurable real-world impact
3. Production-Tested: Healthcare AI, cloud infrastructure, enterprise systems
4. GPU Specialist: Achieved 12.3× improvements through systematic optimization
5. Hands-On Infrastructure: Built Kubernetes clusters, designed ML pipelines, deployed at scale
6. Open Source Leader: 40+ repos, active in AI safety and benchmarking communities

I'm actively seeking opportunities in ML Engineering, Deep Learning, LLM/GenAI, and Cloud Architecture roles. Whether you're building frontier AI systems, scaling ML infrastructure, or advancing AI safety—let's talk!