Anuj Kumar
Open to work3 years experienceAI/ML Engineer
AI/ML Engineer with 3 years experience in architecting end-to-end machine learning pipelines and AI applications.
Standing Rank
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Skills & Technologies
38 skillsWork Experience
Data Scientist
Prism Infoways Private Limited
- Designed and deployed production Retrieval-Augmented Generation (RAG) systems using semantic chunking , hybrid retrieval (BM25 + dense vector embeddings) , and optimized retrieval pipelines, reducing LLM token consumption and client inference costs while improving response quality.
- Developed end-to-end machine learning pipelines for structured data, covering data preprocessing, automated feature engineering, model training, validation, and scalable inference to support production ML applications.
- Built and optimized NLP and machine learning models for business-specific use cases, including text processing, document understanding, semantic search, and predictive analytics, leveraging modern ML and deep learning techniques.
Projects
CLIP-Style-MultiModal-Retrieval-System
View Project- Implemented core architectural concepts from the CLIP research paper from scratch in PyTorch, constructing a modular dual-encoder framework that maps heterogeneous visual (ResNet-50) and textual (DistilBERT) features into a synchronized 256-D joint embedding space via custom MLP projection heads.
- Implemented a symmetric InfoNCE loss pipeline utilizing Automatic Mixed Precision (AMP / FP16) and gradient accumulation to scale batch throughput on hardware-constrained infrastructure.
- Achieved a generalized Text-to-Image Recall@10 of 54.1% (Median Rank: 9) and Image-to-Text Recall@10 of 60.8% (Median Rank: 6) on thousands of unseen test vectors, mitigating severe validation overfitting through data regularization and learning rate scheduling.
Credit Risk Prediction System
View Project- Built an end-to-end ML pipeline to predict loan default risk using the LendingClub dataset (2.2M records, 151 features), including data cleaning, EDA, and feature engineering.
- Trained and evaluated Logistic Regression and XGBoost models with hyperparameter tuning, achieving a ROC-AUC of 0.709 on the test set.
- Developed a modular pipeline covering preprocessing, feature engineering, model training, and evaluation.
- Deployed real-time prediction service using FastAPI, containerized with Docker for reproducibility.
- Implemented CI pipelines via GitHub Actions and wrote unit tests using Pytest and FastAPI TestClient.
- Designed system architecture supporting model retraining and version management.
Leaderboard Standings
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Assessment Highlights
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AI Collaboration Score
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Role Compatibility Profile
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Achievements
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About Details
Professional Bio
AI/ML Engineer with 3 years experience in architecting end-to-end machine learning pipelines, deep learning retrieval systems, and production-grade RAG AI workflows. Specialized in building low-latency inference pipelines via modern MLOps practices.
Bachelor of Technology in Electrical Engineering
Guru Jambheshwar University of Science and Technology (2019 - 2023)