Aman Gupta | Machine Learning Engineer
Building Production-Grade LLM & AI Systems | Cloud (AWS, GCP, Azure) | RAG • LangChain • PyTorch • TensorFlow
I am a Machine Learning Engineer passionate about building and deploying AI-driven solutions that solve real-world problems. With hands-on experience in large language models (LLMs), retrieval-augmented generation (RAG), and deep learning frameworks like PyTorch and TensorFlow, I focus on delivering production-grade systems that scale.
My background spans cloud platforms (AWS, GCP, Azure), MLOps pipelines, and end-to-end model development—from data preprocessing and feature engineering to model training, deployment, and monitoring. I have also worked on integrating LangChain-based applications, enhancing AI-powered solutions with context-aware capabilities.
Beyond technical expertise, I thrive in collaborative environments where I can align cutting-edge research with business needs, ensuring solutions are not only innovative but also impactful.
I’m always eager to explore emerging technologies in AI/ML, cloud-native engineering, and automation, while contributing to teams that value innovation, scalability, and performance.
AI/ML & Data Science: PyTorch · TensorFlow · Scikit-learn · Hugging Face · LangChain · LLMs (Llama 3, GPT-4o) · RAG · Vision AI (OpenCV, GANs) · MLflow Cloud & MLOps: AWS (SageMaker, S3, Bedrock) · Azure (Azure ML, OpenAI) · GCP (Vertex AI, GKE) · Docker · Kubernetes · CI/CD (GitHub Actions) · Airflow · DVC Frameworks & APIs: FastAPI · Flask · gRPC · OpenAPI Databases: PostgreSQL (pgvector) · MySQL · MS SQL Server · MongoDB · Snowflake · Vector DBs (Milvus, Weaviate) · Redis Big Data & Distributed Systems: Apache Spark · Apache Kafka · Hadoop · Dask · Ray Languages: Python · R · SQL · C++ · Bash
📂 Professional Experience
- Developed fraud detection models in Python & PyTorch, boosting accuracy by 22% and cutting false positives by 30%.
- Deployed models as low-latency APIs with Flask & FastAPI, reducing inference time by 30% for real-time applications.
- Built a reproducible MLOps pipeline using Docker, MLflow, and AWS SageMaker, accelerating deployment cycles by 40%.
- Enhanced model generalization by 18% through advanced feature engineering and hyperparameter tuning.
- Built supply chain forecasting models with Scikit-learn, improving prediction accuracy by 18%.
- Automated document classification using spaCy & Hugging Face, slashing manual processing workload by 40%.
- Developed a computer vision system with OpenCV, increasing manufacturing defect detection accuracy by 25%.
- Engineered end-to-end ML workflows on Snowflake & BigQuery, cutting data processing time by 35%.
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M.S. in Machine Learning, Stevens Institute of Technology (Dec 2024)
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B.Tech in Computer Science, Kurukshetra University (May 2022)
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AWS Certified Data Engineering Associate
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Microsoft Certified AI Engineer Associate
Feel free to reach out for collaborations on backend systems, cloud-native data platforms, or AI/ML integrations!
