I am a Data Systems and ML/LLM Engineer focused on building reliable systems at the intersection of distributed data engineering, machine learning infrastructure, and applied AI.
My work spans high-throughput batch and streaming pipelines, lakehouse platforms, GPU-accelerated ML/LLM workflows, semantic retrieval, multimodal modeling, deployment, monitoring, and production optimization on AWS and Azure.
I care about measurable performance, reproducible experimentation, robust system design, and taking products from raw data to deployed intelligence.
Data Engineer · ML Engineer · ML Platform Engineer · Applied ML/LLM Engineer · Data Scientist
| Outcome | Engineering System |
|---|---|
| 50% faster queries and 40% less storage | Apache Hudi optimization for large-scale analytical workloads |
| 0.542 seconds per evaluation sample | Sharded multi-GPU evaluation for a 4B-parameter multimodal model |
| 1.4B of 4B parameters active per route | Top-2 Mixture-of-Experts routing in KAIROS |
| 0.81 AUC and 0.73 recall | Distributed truck-delay prediction with GPU-accelerated XGBoost |
| IC 0.09 and ICIR 0.62 | Regime-aware alpha research across 520 equities |
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Multimodal driving-scene reasoning with a hybrid SSM architecture
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Distributed, regime-aware financial ML and backtesting platform
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High-throughput e-commerce intelligence and real-time threat detection
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Scalable truck-delay prediction, deployment, and monitoring
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Sentence-BERT FAISS Embeddings Vector Search Flask Docker
- Built a semantic retrieval platform indexing 2M+ vectors with Sentence-BERT and FAISS.
- Developed scalable embedding, batching, indexing, evaluation, and low-latency top-K retrieval workflows.
- Exposed retrieval through a containerized API for high-throughput query processing.
| Domain | Technologies |
|---|---|
| Data Engineering | Apache Spark, PySpark, Kafka, Flink, Kinesis, Databricks, Delta Lake, Apache Hudi, ETL/ELT |
| ML & LLM Systems | PyTorch, Hugging Face Transformers, Spark ML, XGBoost4J-Spark, RAPIDS, LoRA, DeepSpeed, Mamba-2, MoE |
| Search & Retrieval | Sentence-BERT, FAISS, embeddings, vector indexing, semantic search, top-K retrieval |
| Cloud & MLOps | AWS, Azure, SageMaker, Azure ML, Azure Data Factory, Azure DevOps, Evidently AI, Docker, CI/CD |
| Backend & Storage | C#, .NET, Python, SQL, PostgreSQL, Flask, event-driven services |
December 2022 – December 2023
- Implemented backend services with C#/.NET and PostgreSQL for event-driven workflows, scoring systems, and real-time leaderboards.
- Contributed across software engineering, data pipelines, and ML workflows for production systems deployed on Azure.
- Collaborated across engineering functions to improve system integration, reliability, and delivery.
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B.E. in Computer Science and Engineering |
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I am interested in teams developing distributed data platforms, real-time analytics, ML/LLM infrastructure, multimodal systems, and large-scale retrieval platforms—especially roles where I can contribute across ingestion, processing, training, evaluation, deployment, observability, and continuous improvement.
