Machine Learning Data Science

AWIN ANWAR

Machine Learning, Data Science Generative AI.

860-***-**** ***********@*****.*** Hartford, CT(06104),USA Summary

Machine Learning and Data Science expert with 11 years of experience in predictive analytics, deep learning, generative AI, LLMs, NLP, computer vision, and MLOps. Skilled in Python, PyTorch, TensorFlow, Scikit-learn, SQL, Spark, and cloud (AWS, GCP, Azure). Proven success in supervised/unsupervised learning, recommendation systems, time series, and prompt engineering. Led end-to-end AI/ML model development, deployment, and optimization for real-world business solutions. Skills

Strategic IT Planning Supervised Learning Unsupervised Learning Deep Learning CNN RNN LSTM Transformers Reinforcement Learning Natural Language Processing NLP Computer Vision Time Series Forecasting Recommendation Systems Anomaly Detection Transfer Learning Prompt Engineering LLM Fine-Tuning GPT Mistral MLflow DVC Airflow Prefect Docker Kubernetes FastAPI Flask Model Monitoring Prometheus Grafana CI/CD GitHub Actions Jenkins GitLab CI/CD RESTful APIs Streamlit Dash AWS SageMaker Lambda EC2 S3 Athena Redshift Google Cloud Vertex AI BigQuery Dataflow Azure Azure ML Synapse Terraform CloudFormation Docker Composes Experience

Mu Sigma

Lead Machine Learning Engineer – Generative AI 03/2024 - Present Designed and deployed supervised and unsupervised ML models for classification, regression, and clustering tasks using Python, Scikit-learn, XGBoost, and LightGBM.

Built scalable ML pipelines using MLflow, Airflow, and Docker for automated training, validation, and deployment. Optimized model performance through feature engineering, hyperparameter tuning, and cross-validation. Deployed models to production environments using AWS SageMaker, GCP Vertex AI, and Azure ML. NVIDIA

Senior Data Scientist / Data Analyst 02/2021 - 01/2023 Company Description

Led analytical projects integrating machine learning with business intelligence to drive actionable insights and data-driven decision-making. Conducted end-to-end data exploration, cleaning, and visualization using Python, SQL, and BI tools to identify trends, anomalies, and growth opportunities.

Built predictive models for customer churn, sales forecasting, and user behavior analysis using Scikit-learn and statistical methods. Designed automated data reporting pipelines using Pandas, Airflow, and Power BI, improving efficiency and reducing manual workload. Developed dashboards and visualizations for cross-functional teams to monitor KPIs, track campaign performance, and evaluate product usage. Collaborated with stakeholders to define business metrics, establish data governance practices, and implement A/B testing strategies. Partnered with data engineers and ML teams to integrate analysis outputs into product features and ML pipelines. Amazon

Machine Learning Engineer – AI/ML 04/2019 - 12/2020 Company Description

Leading the design, development, and deployment of scalable machine learning solutions to support AI-driven personalization, fraud detection, and customer intelligence systems.

Built production-ready ML models using Python, Scikit-learn, TensorFlow, and PyTorch, integrated with AWS services like SageMaker, Lambda, Step Functions, and Redshift.

Developed and optimized deep learning pipelines for NLP and computer vision use cases including product categorization, sentiment analysis, and image tagging.

Designed custom GenAI components using Amazon Bedrock, OpenAI (GPT-4), and fine-tuned LLaMA models for internal tools and intelligent automation.

Implemented MLOps pipelines with MLflow, Docker, Airflow, and CloudWatch for model versioning, monitoring, and retraining. Collaborated with cross-functional teams (product, data engineering, operations) to align ML initiatives with business objectives, improving decision-making across multiple domains.

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Experience

Fractal Analytics

Machine Learning Engineer 04/2014 - 03/2019

Company Description

Designed and deployed large-scale machine learning solutions across e-commerce, customer behavior analytics, and operations optimization. Built and maintained ML models for recommendation systems, fraud detection, and predictive analytics using Python, Scikit-learn, XGBoost, and AWS SageMaker.

Developed scalable deep learning models with TensorFlow and PyTorch for NLP and computer vision tasks, including product tagging, sentiment analysis, and language understanding.

Delivered GenAI-based solutions using Amazon Bedrock, OpenAI (GPT-4), and fine-tuned LLaMA models for use cases like knowledge assistants, smart search, and customer support automation.

Architected MLOps pipelines for continuous training, evaluation, deployment, and monitoring using Airflow, MLflow, Docker, and AWS services. Collaborated with data engineers, scientists, and product managers to define AI-driven features and integrate them into high-availability production systems.

Applied prompt engineering and fine-tuning techniques to improve LLM output relevance, reduce latency, and ensure responsible AI deployment.

Education

University of Management and Technology

Bachelor of Science in Computer Science

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