• Engineered next-generation LLM backend leveraging Llama 3.3-70B and speculative decoding, enhancing factual Q&A reliability and massively scaling inference throughput across Cerebras clusters.

• Designed and implemented Mixture-of-Experts (MoE) routing and FP8 quantization pipelines, reducing inference latency and optimizing compute efficiency across large-scale distributed GPU instances.

• Architected Retrieval-Augmented Generation (RAG) stack with FAISS, BM25, and LangChain, boosting contextual precision by 20% and reducing hallucination rate by 18% on LM Arena benchmarks.

• Automated benchmark evaluation using MMLU, TruthfulQA, and GSM8K, improving F1 scores by 13% and cutting operational cost by 38% through optimized AWS SageMaker workloads.

• Deployed Sonar Pro across AWS EKS and Triton Inference Server, ensuring high-availability uptime and driving significant growth in API adoption across 1,200+ enterprise developers.

• Leveraged PyTorch, DeepSpeed, vLLM, and FlashAttention-3 for distributed fine-tuning, implementing LoRA, SFT, and RLHF pipelines to ensure efficient multi-node model deployment on AWS Cloud.

• Built hybrid retrieval systems integrating FAISS, ElasticSearch, and LangChain, optimizing response latency and contextual recall for large-scale factual Q&A applications.

• Utilized AWS Lambda, Step Functions, and EFS for orchestration of inference workflows, improving scalability and fault-tolerance across multi-cluster LLM serving pipelines.

• Monitored distributed inference with Prometheus, Datadog, and Weights & Biases, enabling automated alerting and 25% improvement in observability and cost-performance balance.

• Applied speculative decoding, MoE routing, RAG orchestration, FlashAttention, and self-verification frameworks to enhance model reasoning reliability and drive enterprise-grade AI adoption.

• Engineered large-scale training pipelines for LLaMA 3 (70B–130B) using PyTorch 2.2, DeepSpeed, and AWS Cloud, enabling faster throughput and reduced compute cost across 12K GPU clusters.

• Optimized multimodal architecture by integrating text-vision encoders with FP8 quantization on AWS

infrastructure, enhancing inference efficiency and improving cross-modal accuracy on internal benchmarks.

• Implemented RLHF + DPO fine-tuning pipelines with human feedback curation, enhancing alignment safety metrics by 27% and reducing hallucination rate in production responses by 33%.

• Collaborated on the open-source LLaMA 3.2 release, developing deployment scripts and evaluation suites on AWS, improving reproducibility and accelerating global community adoption beyond 1.7M downloads.

• Built distributed data-processing workflows on AWS EMR and S3 using Spark, Ray, PyArrow, and Hydra for trilliontoken multilingual datasets, ensuring high reliability in continuous large-scale ingestion pipelines.

• Deployed optimized inference stack via Triton Inference Server, ONNX Runtime, TorchServe, leveraging MTIA v2 and H100 GPUs for latency-aware serving across Meta AI Assistant endpoints.

• Automated monitoring and performance analytics with Prometheus, Grafana, and Meta AI Dashboards, enabling predictive scaling and reducing infrastructure incidents by 22% in training clusters.

• Applied MLOps and DevOps practices using Kubernetes, Docker, CI/CD (GitHub Actions), accelerating

experimentation cycles by 35% and improving model deployment stability across environments.

• Utilized advanced AI stack Transformers, PyTorch Lightning, LangChain, Weights & Biases, TensorRT, Faiss, LLM Ops frameworks driving reproducible, scalable, and explainable model delivery in 2025 AI ecosystem.

• Built scalable knowledge graph embeddings on AWS Cloud using TensorFlow and Keras, assisting research teams in optimizing TransE and ComplEx models, boosting link prediction accuracy by 32%.

• Developed automated ML pipelines for training, evaluation, and deployment using MLflow and Docker, supporting faster experimentation and improving reproducibility by 45% across distributed cloud environments.

• Contributed APIs and integration modules to AmpliGraph’s open-source release on AWS, assisting enterprise teams in seamless adoption for large-scale graph analytics and AI-driven insights.

• Tuned GPU-based distributed training setups and analyzed performance metrics to enhance convergence rates, supporting cost-efficient optimization that reduced cloud compute expenses by 18%.

• Applied advanced Graph ML and embedding techniques using Python, TensorFlow, PyTorch, and Keras, assisting data scientists in extracting relational patterns from structured and semi-structured datasets.

• Designed and implemented MLOps pipelines leveraging Docker, Airflow, and MLflow, supporting CI/CD automation, reproducible experiments, and robust retraining workflows in production.

• Integrated AWS SageMaker, Azure ML, and Vertex AI for model lifecycle management, analyzing Generative AI and LLM capabilities to support knowledge graph reasoning and infrastructure scalability.