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Hi, I’m Suryaprakash Halwasia, an AI/ML Engineer with 4+ years of experience building production-grade machine learning systems, LLM-powered applications, and distributed data platforms across enterprise environments. I specialize in Generative AI, NLP, RAG pipelines, embeddings, PyTorch, TensorFlow, XGBoost, and real-time inference systems, with strong expertise in Python, AWS, and MLOps. I thrive on turning data into reliable, scalable ML solutions that drive business value.…Hi, I’m Suryaprakash Halwasia, an AI/ML Engineer with 4+ years of experience building production-grade machine learning systems, LLM-powered applications, and distributed data platforms across enterprise environments. I specialize in Generative AI, NLP, RAG pipelines, embeddings, PyTorch, TensorFlow, XGBoost, and real-time inference systems, with strong expertise in Python, AWS, and MLOps. I thrive on turning data into reliable, scalable ML solutions that drive business value.

Suryaprakash Sushilkumar Halwasia

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Hi, I’m Suryaprakash Halwasia, an AI/ML Engineer with 4+ years of experience building production-grade machine learning systems, LLM-powered applications, and distributed data platforms across enterprise environments. I specialize in Generative AI, NLP, RAG pipelines, embeddings, PyTorch, TensorFlow, XGBoost, and real-time inference systems, with strong expertise in Python, AWS, and MLOps. I thrive on turning data into reliable, scalable ML solutions that drive business value.…Hi, I’m Suryaprakash Halwasia, an AI/ML Engineer with 4+ years of experience building production-grade machine learning systems, LLM-powered applications, and distributed data platforms across enterprise environments. I specialize in Generative AI, NLP, RAG pipelines, embeddings, PyTorch, TensorFlow, XGBoost, and real-time inference systems, with strong expertise in Python, AWS, and MLOps. I thrive on turning data into reliable, scalable ML solutions that drive business value.

Available to hire

Hi, I’m Suryaprakash Halwasia, an AI/ML Engineer with 4+ years of experience building production-grade machine learning systems, LLM-powered applications, and distributed data platforms across enterprise environments. I specialize in Generative AI, NLP, RAG pipelines, embeddings, PyTorch, TensorFlow, XGBoost, and real-time inference systems, with strong expertise in Python, AWS, and MLOps. I thrive on turning data into reliable, scalable ML solutions that drive business value.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Jedi Ark Corp

July 1, 2025 - Present

Architected ML and RAG pipelines using PyTorch, TensorFlow, and Scikit-Learn, processing millions of records and improving data quality and model readiness by 70% through automated validation, anomaly detection, and feature engineering. Built LLM-powered data intelligence systems using embeddings and vector search to generate contextual insights across enterprise datasets. Designed feature engineering pipelines and model training workflows with XGBoost for anomaly detection and predictive analytics. Implemented model evaluation, hyperparameter tuning, and cross-validation. Developed real-time inference pipelines and deployment systems using FastAPI and AWS SageMaker. Integrated model monitoring and observability to maintain performance and reliability.

AI/ML Engineer at Juego Juegos

February 1, 2025 - May 31, 2025

Built scalable ML-ready backend systems supporting 7K+ users and 50K+ API calls/month, enabling real-time analytics and recommendations. Developed data pipelines, ETL workflows using AWS Redshift, Lambda, and Airflow for ML model readiness. Integrated ML inference pipelines and predictive analytics workflows using AWS SageMaker and REST APIs. Designed event-driven architecture with Kafka and distributed systems for real-time data streaming and ML pipeline execution. Reduced deployment time by 65% by implementing CI/CD pipelines and automated model deployment workflows.

Software Engineer at Zensar

July 1, 2020 - July 1, 2023

Built large-scale data platform supporting 500K+ users and millions of transactions, enabling ML-driven analytics and predictive modeling. Designed data preprocessing, feature engineering, and transformation pipelines for anomaly detection and customer behavior modeling. Developed backend APIs supporting ML inference pipelines and data access layers, reducing latency by 20%. Applied machine learning models (XGBoost, Scikit-learn) for predictive insights and automation workflows. Optimized data systems reducing retrieval time by 25%, enabling faster model execution and inference. Deployed ML-ready systems on AWS with 99.9% uptime, supporting scalable model deployment and distributed workloads.