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Results-driven technology professional with 4 years of experience building and operationalizing Machine learning, data science, and AI solutions in enterprise environments. Experienced in designing, developing, and deploying scalable ML models and analytics systems, including data collection, EDA, feature engineering, statistical modeling, machine learning and deep learning, hyperparameter tuning, evaluation, deployment, and monitoring. Hands-on expertise in predictive analytics, classification, regression, clustering, anomaly detection, and decision-support systems using structured and unstructured data. Additionally skilled in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and AIOps, with practical exposure to MLOps and LLMOps practices, cloud-native AI platforms, vector databases, and AI observability.…Results-driven technology professional with 4 years of experience building and operationalizing Machine learning, data science, and AI solutions in enterprise environments. Experienced in designing, developing, and deploying scalable ML models and analytics systems, including data collection, EDA, feature engineering, statistical modeling, machine learning and deep learning, hyperparameter tuning, evaluation, deployment, and monitoring. Hands-on expertise in predictive analytics, classification, regression, clustering, anomaly detection, and decision-support systems using structured and unstructured data. Additionally skilled in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and AIOps, with practical exposure to MLOps and LLMOps practices, cloud-native AI platforms, vector databases, and AI observability.

Jasmeet Singh Sainiai

AI Engineer, Data Scientist, AI Developer, +4

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Results-driven technology professional with 4 years of experience building and operationalizing Machine learning, data science, and AI solutions in enterprise environments. Experienced in designing, developing, and deploying scalable ML models and analytics systems, including data collection, EDA, feature engineering, statistical modeling, machine learning and deep learning, hyperparameter tuning, evaluation, deployment, and monitoring. Hands-on expertise in predictive analytics, classification, regression, clustering, anomaly detection, and decision-support systems using structured and unstructured data. Additionally skilled in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and AIOps, with practical exposure to MLOps and LLMOps practices, cloud-native AI platforms, vector databases, and AI observability.…Results-driven technology professional with 4 years of experience building and operationalizing Machine learning, data science, and AI solutions in enterprise environments. Experienced in designing, developing, and deploying scalable ML models and analytics systems, including data collection, EDA, feature engineering, statistical modeling, machine learning and deep learning, hyperparameter tuning, evaluation, deployment, and monitoring. Hands-on expertise in predictive analytics, classification, regression, clustering, anomaly detection, and decision-support systems using structured and unstructured data. Additionally skilled in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and AIOps, with practical exposure to MLOps and LLMOps practices, cloud-native AI platforms, vector databases, and AI observability.

Available to hire

Results-driven technology professional with 4 years of experience building and operationalizing Machine learning, data science, and AI solutions in enterprise environments. Experienced in designing, developing, and deploying scalable ML models and analytics systems, including data collection, EDA, feature engineering, statistical modeling, machine learning and deep learning, hyperparameter tuning, evaluation, deployment, and monitoring.

Hands-on expertise in predictive analytics, classification, regression, clustering, anomaly detection, and decision-support systems using structured and unstructured data. Additionally skilled in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and AIOps, with practical exposure to MLOps and LLMOps practices, cloud-native AI platforms, vector databases, and AI observability.

Skills

Experience Level

Expert

Expert

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Expert

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

AI Engineer at Vedansta

May 1, 2024 - Present

Assisted in fine-tuning Transformer-based NLP models (Hugging Face, PyTorch) for SMB automation use cases, improving annotation quality with basic GenAI techniques under senior guidance. Supported automation of ML workflows using Apache Airflow and GitHub Actions to streamline model retraining and deployment in a SaaS environment. Helped containerize AI microservices with Docker and contributed to Kubernetes deployments, improving platform reliability. Assisted in building computer vision pipelines using YOLOv8 and Detectron2 for automated image labeling and object detection. Participated in benchmarking LLMs (GPT-4, Claude, Mistral) to optimize costs and improve chatbot response relevance for customer engagement.

AI / ML Engineer at Mphasis

October 1, 2021 - November 1, 2022

Developed and deployed ML/AI models for fraud detection, transaction risk scoring, user analytics, and automated decisioning across the full lifecycle (ingestion, EDA, feature engineering, training, evaluation, deployment, monitoring) using structured and unstructured data. Implemented supervised ML and deep learning models using PyTorch and TensorFlow for classification/intent detection, applying ensemble learning, hyperparameter tuning, cost-sensitive learning, threshold optimization, and feature selection to improve precision-recall in high-risk financial systems. Supported scalable deployment on AWS (S3, EC2, Lambda, SageMaker) and applied MLOps best practices including CI/CD, model versioning, drift detection, automated retraining, AI observability, and explainability (SHAP), contributing to reusable frameworks and production stability.

Data Scientist at Hexaware

January 1, 2020 - September 1, 2021

Analyzed large structured and semi-structured datasets including data collection, cleaning, preprocessing, and exploratory data analysis (EDA) to identify patterns, anomalies, trends, and issues using statistical analysis and hypothesis testing. Built and validated supervised and unsupervised models (regression, classification, clustering), using feature engineering/selection to improve performance, interpretability, and business impact. Used Python, SQL, Pandas, NumPy, and scikit-learn for experimentation, evaluation, and cross-validation; created dashboards and reports in Power BI and Tableau. Assisted with deployment of ML/analytics solutions on AWS and GCP, focusing on data quality, governance, documentation, and collaboration in Agile/Scrum.