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Results-driven AI/ML Engineer with 3+ years of experience deploying machine learning, data science, and Generative AI solutions in enterprise environments. Skilled across EDA, feature engineering, statistical modeling, ML/DL development, hyperparameter tuning, evaluation, and monitoring. Hands-on expertise in LLMs, RAG, and AI agents, along with AIOps, MLOps/LLMOps, LLMOps, cloud-native AI platforms, vector databases, and AI observability—building intelligent search, conversational AI, and forecasting systems.…Results-driven AI/ML Engineer with 3+ years of experience deploying machine learning, data science, and Generative AI solutions in enterprise environments. Skilled across EDA, feature engineering, statistical modeling, ML/DL development, hyperparameter tuning, evaluation, and monitoring. Hands-on expertise in LLMs, RAG, and AI agents, along with AIOps, MLOps/LLMOps, LLMOps, cloud-native AI platforms, vector databases, and AI observability—building intelligent search, conversational AI, and forecasting systems.

Jasmeet Singh Saini

AI Engineer, Data Scientist, Software Engineer, +4

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Results-driven AI/ML Engineer with 3+ years of experience deploying machine learning, data science, and Generative AI solutions in enterprise environments. Skilled across EDA, feature engineering, statistical modeling, ML/DL development, hyperparameter tuning, evaluation, and monitoring. Hands-on expertise in LLMs, RAG, and AI agents, along with AIOps, MLOps/LLMOps, LLMOps, cloud-native AI platforms, vector databases, and AI observability—building intelligent search, conversational AI, and forecasting systems.…Results-driven AI/ML Engineer with 3+ years of experience deploying machine learning, data science, and Generative AI solutions in enterprise environments. Skilled across EDA, feature engineering, statistical modeling, ML/DL development, hyperparameter tuning, evaluation, and monitoring. Hands-on expertise in LLMs, RAG, and AI agents, along with AIOps, MLOps/LLMOps, LLMOps, cloud-native AI platforms, vector databases, and AI observability—building intelligent search, conversational AI, and forecasting systems.

Available to hire

Results-driven AI/ML Engineer with 3+ years of experience deploying machine learning, data science, and Generative AI solutions in enterprise environments. Skilled across EDA, feature engineering, statistical modeling, ML/DL development, hyperparameter tuning, evaluation, and monitoring.

Hands-on expertise in LLMs, RAG, and AI agents, along with AIOps, MLOps/LLMOps, LLMOps, cloud-native AI platforms, vector databases, and AI observability—building intelligent search, conversational AI, and forecasting systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

AI Engineer at Vedansta

May 1, 2025 - Present

Fine-tuned Transformer-based NLP models for SMB automation using Hugging Face and PyTorch, improving annotation quality via GenAI techniques and prompt engineering. Implemented RAG pipelines with embeddings, vector databases, retrieval validation, and connections to GenAI APIs for intelligent search and document summarization. Automated ML workflows using Apache Airflow and GitHub Actions; supported MLOps/LLMOps tasks including experiment tracking, retraining, deployment validation, monitoring, and AI observability. Containerized AI microservices using Docker and Kubernetes for production stability. Built computer vision pipelines (YOLOv8, Detectron2) for automated image labeling and object detection, and benchmarked LLMs (GPT-4, Claude) for cost optimization and response evaluation.

AI / ML Engineer at Mphasis

October 1, 2021 - November 1, 2022

Developed and deployed ML/AI solutions for fraud detection, transaction risk scoring, user analytics, and automated decisioning across the full lifecycle (ingestion, EDA, feature engineering, training, evaluation, deployment, monitoring). Built supervised ML and deep learning models in PyTorch and TensorFlow for classification and intent detection using ensemble learning, hyperparameter tuning, cost-sensitive learning, and threshold optimization. Deployed solutions on AWS (S3, EC2, Lambda, SageMaker) and implemented MLOps practices such as CI/CD, model versioning, drift detection, automated retraining, AI observability, and explainability with SHAP.

Data Scientist at Hexaware

January 1, 2020 - September 1, 2021

Performed analysis of large structured and semi-structured datasets including data collection, cleaning, preprocessing, and EDA to identify patterns, anomalies, and trends using statistical analysis and hypothesis testing. Built and validated supervised and unsupervised ML models (regression, classification, clustering) with feature engineering and selection to improve performance and interpretability. Used Python, SQL, Pandas, NumPy, and scikit-learn for experimentation and evaluation, and created Power BI/Tableau dashboards to support data-driven decision-making while deploying solutions on AWS and GCP.