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Mohammed Numair Ahmed - Senior Data Scientist | AI/ML Engineer

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Location: Remote, Remote, USA

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Mohammed Numair Ahmed Senior Data Scientist | AI/ML Engineer Phone: (940) 340-9312 Email: [email protected] LinkedIn: https://www.linkedin.com/in/mohammed-numair-356a59222/ PROFESSIONAL SUMMARY Senior Data Scientist with 10+ years implementing enterprise-scale machine learning platforms using Python, TensorFlow, PyTorch, and scikit-learn, specializing in deep learning and neural network architectures that delivered 70% model accuracy improvement across operations through ensemble methods optimization, hyperparameter tuning, model deployment, MLOps pipelines, and performance monitoring with MLflow tracking. Developed expertise in Generative AI and Large Language Models (LLMs) for building conversational AI solutions with transformer architectures and attention mechanisms, optimizing GPT models and BERT implementations that achieved 45% inference speed improvement through automated model compression techniques using quantization, pruning, ONNX optimization, containerization workflows, and Kubernetes orchestration for scalable deployments. Orchestrated Natural Language Processing (NLP) for enterprise text analytics workflows using spaCy, NLTK, Hugging Face Transformers, and OpenAI API, LangChain for RAG implementations, and vector databases serving 100TB+ daily processing of text data, audio processing, and computer vision datasets while maintaining 95% model performance through feature engineering frameworks, data augmentation, and cross-validation patterns. Implemented leadership in Computer Vision and Deep Learning enterprise implementations, migrating 300+ legacy models and establishing automated ML pipeline capabilities using OpenCV, YOLO, ResNet architectures, and CNN optimization that improved operational efficiency by 80% through Python automation, Docker containerization, AWS SageMaker, and model versioning integration with A/B testing handling. Designed MLOps Architecture solutions leveraging AWS SageMaker with model registry governance integration, enabling predictive analytics and real-time inference across 700+ models while delivering $3M annual savings via automated model training, statistical modeling, feature store management with experiment tracking, model monitoring workflows, and Jupyter notebooks visualization dashboards for data exploration. Configured machine learning ecosystems using Apache Spark and PySpark, implementing Python distributed computing workflows with MLlib, feature engineering, and large-scale model training that handled 20TB+ daily datasets of structured data, time series data, and unstructured data while achieving 60% faster training cycles through gradient descent algorithms, batch processing optimization, hyperparameter optimization, and automated scheduling via Apache Airflow. Streamlined enterprise AI architecture using AWS Lambda orchestration with Amazon Bedrock integration, automating model deployment from 150+ heterogeneous sources including APIs, streaming data, IoT sensors, and social media feeds while maintaining regulatory compliance through AWS IAM security protocols, data privacy, model explainability, bias detection, ethical AI practices, and governance frameworks using automated testing and CI/CD pipelines. Enhanced real-time ML inference capabilities leveraging Amazon Kinesis with AWS Lambda integration and Apache Kafka, processing 1M+ predictions per second of streaming data and multi-modal data integration while maintaining 99.9% high availability through automated model rollback mechanisms, load balancing, performance optimization using GPU acceleration, model optimization strategies, CloudWatch monitoring, Prometheus dashboards, and observability analytics. Validated MLOps practices implementing GitHub Actions with Docker containerization and Kubernetes orchestration, establishing CI/CD pipelines for model deployment while maintaining 99.8% system reliability through automated testing frameworks, infrastructure as code, model configuration management, Git version control strategies, continuous integration/continuous delivery workflows, model provisioning and orchestration, Linux systems administration, shell scripting, and model artifact optimization. TECHNICAL SKILLS Programming Languages Python, JAVA, JavaScript, jQuery, ReactJS, Next.js, HTML, CSS, C, C++, Angular, R, Impala, Hive, SQL, Go, GoLang. Machine Learning Supervised Learning, Unsupervised Learning, Model Evaluation, Cross-Validation, Feature Engineering, Hyperparameter Tuning, Regularization (L1, L2), Ensemble Methods, Decision Trees, Random Forest, SVM, KNN, Regression, Classification, Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA, t-SNE), Time Series Forecasting, Model Interpretability (SHAP, LIME. MLOps & LLMOps Model Registry, Containerization (Docker), Model Monitoring, Scalable LLM Deployment (TGI, vLLM), Experiment Tracking (MLflow, Weights & Biases), Model Deployment (REST API, Batch), Feature Stores, Orchestration (Kubernetes), ML Pipelines (MLflow, Airflow, Kubeflow), CI/CD for ML, Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, Azure AI), Data Versioning (DVC). Natural Language Processing (NLP) Bag of Words (BoW), Sequence-to-Sequence Models, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Attention Mechanism, Encoder-Decoder Models, Gated Recurrent Units (GRU), Transformer Models (BERT, GPT, T5), Named Entity Recognition (NER), Sentiment Analysis, Machine Translation, Hugging Face. Generative AI LLM s, Ollama, Langchain, Langsmith, Agentic AI, Fine-tuning Techniques (LoRA, QLoRA), Retrieval-Augmented Generation (RAG), Graph Databases(Neo4j), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Conditional GANs, CycleGAN, StyleGAN, Deep Convolutional GANs (DCGAN), Transformer-based Generative Models (GPT, T5), Text-to-Image Generation (DALL-E, CLIP), Image-to-Image Translation, Neural Style Transfer, Chatbots, AI Search Engines. Deep Learning Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Autoencoders, Transfer Learning (VGG, ResNet, InceptionNet, MobileNet), Attention Mechanism, Object Detection (YOLO, Faster R-CNN), OCR, Image Segmentation (U-Net, Mask R-CNN), Optimization Algorithms (SGD, Adam, RMSprop), Loss Functions (Cross-Entropy, MSE), Model Deployment, TensorFlow, Keras, PyTorch. Big Data: Hadoop, Hive, HBase, Apache Spark, Scala, Kinesis, Pig, Sqoop. Amazon Web Services: EC2, Lambda, Sage Maker, Bedrock, EMR, S3, Glue, MKS, Kinesis, Quick Sight, API Gateway, Athena, Lex, Recognition, CI/CD, Code Commit, DynamoDB, transcribe, Cloud Formation, Cloud Watch, Glacier, IAM. Database Servers: MySQL, Mongo Microsoft SQL server, SQLite, Red Shift, RDS, PostgreSQL, Mongo DB, Teradata. Other Tools & Technologies: Git, GitHub, GitLab, Docker, Docker Compose, Kubernetes, VS Code, Jupyter Notebook, Google Colab, CUDA Toolkit, Postman, Swagger, REST APIs, Linux/Unix Command Line, Bash Scripting, Conda, Virtualenv, Makefile, YAML, JSON, Terraform, Nginx, Redis, Jenkins, Power BI, Tableau, Figma, GCP, Azure AI. PROFESSIONAL EXPERIENCE Client: Community Health System, Franklin, TN | Aug 2023 to Present Role: Senior Data Scientist | GenAI Engineer Responsibilities: Built scalable ML pipelines using AWS SageMaker Pipelines, Step Functions, and Lambda to automate model training, hyperparameter tuning, validation, and deployment for healthcare predictive analytics. Designed RAG-based clinical knowledge retrieval systems using LangChain, embeddings, and vector search to enable contextual querying across EHRs, clinical notes, and medical literature. Fine-tuned large language models including Claude and GPT 4 using parameter efficient tuning techniques such as LoRA and PEFT to automate discharge summary generation call transcript summarization clinical documentation assistance and regulatory compliance validation improving documentation efficiency across care teams. Developed and deployed machine learning models including Logistic Regression Random Forest XGBoost Support Vector Machines and KNN to predict patient readmissions detect healthcare fraud identify high risk patient populations and improve operational efficiency within hospital systems. Engineered clinical natural language processing pipelines using BioBERT ClinicalBERT and scispaCy to extract medical entities including diagnoses medications symptoms procedures and clinical events from unstructured physician notes discharge summaries and patient records. Integrated hybrid search architectures combining structured SQL based reasoning engines with LangChain powered unstructured question answering pipelines to deliver highly accurate contextual search results across clinical databases and document repositories. Implemented enterprise scale semantic search solutions using vector embeddings and vector databases to enable real time retrieval of relevant clinical insights across millions of structured and unstructured healthcare records. Applied healthcare terminology standards including ICD 10 SNOMED CT CPT and LOINC to normalize clinical data sources ensuring consistency across analytics reporting predictive modeling and interoperability systems. Established comprehensive MLOps frameworks using AWS SageMaker Model Monitor CloudWatch MLflow and automated CI CD pipelines to track model performance detect drift enforce governance and enable continuous retraining of production models. Designed and implemented scalable ETL pipelines using Python PySpark AWS Glue and Airflow to ingest transform and integrate large scale healthcare datasets into Snowflake based data warehouses for advanced analytics and enterprise reporting. Developed optimized healthcare data models including star schema snowflake schema and normalized relational models to support business intelligence reporting clinical analytics and financial performance monitoring. Implemented explainable AI techniques including SHAP LIME and model interpretability dashboards to ensure transparency and regulatory compliance for clinical AI models used in healthcare decision support environments. Designed and deployed clinical decision support system modules that leverage predictive analytics and generative AI to provide physicians with risk scores diagnostic insights and treatment recommendations. Enabled interoperability between multiple healthcare platforms by integrating FHIR and HL7 based data exchange frameworks supporting standardized ingestion transformation and real time data sharing across healthcare systems. Conducted rigorous model validation A B testing and performance benchmarking of machine learning models and generative AI solutions to continuously improve clinical search accuracy ranking algorithms and predictive performance. Developed AI powered clinical copilots that assist physicians and care coordinators with real time summarization of patient histories automated chart review and contextual recommendations based on clinical guidelines. Mentored junior data scientists and machine learning engineers on best practices for generative AI model development data engineering pipelines experiment tracking and production deployment strategies in healthcare environments. Environment: SDLC, Python, Scikit-learn, Numpy, Scipy, Matplotlib, Pandas, AWS S3, Dynamo DB, and AWS Lambda, AWS EC2, Sage Maker, Lex, EMR, Redshift, Snowflake, RNN, Machine Learning, Deep Learning, OLAP, ODS, OLTP, 3NF, Naive Bayes, Random Forest, K-means clustering, KNN, PCA, Power BI. Client: CVS Health, Irving, TX | Feb 2021 to Aug 2023 Role: AI Engineer Responsibilities: Engineered scalable machine learning and AI solutions to support healthcare analytics clinical decision workflows and operational intelligence ensuring high data integrity and reliability across large scale enterprise healthcare datasets. Utilized Python libraries including Scikit learn TensorFlow and PySpark MLlib to perform advanced feature engineering data preprocessing imputation and feature selection enabling development of high-quality datasets for predictive healthcare analytics. Developed and deployed real time predictive models using TensorFlow Keras and Scikit learn to analyze patient engagement patterns claims activity and operational signals improving healthcare service delivery and risk prediction accuracy. Fine-tuned transformer based large language models using Hugging Face frameworks and LLaMA architectures with LoRA and PEFT techniques to automate clinical document classification healthcare policy analysis and regulatory compliance monitoring. Designed and implemented Retrieval Augmented Generation pipelines using LangChain vector embeddings and semantic search architectures to enable contextual retrieval of clinical documentation medical policies and patient support knowledge bases. Built advanced machine learning models including Ridge Regression Lasso Regression XGBoost and KMeans clustering to predict patient adherence identify high risk populations and improve care management strategies across CVS healthcare services. Integrated AWS cloud services including EC2 S3 Redshift RDS API Gateway ELB SNS and EBS with Google Cloud Vertex AI platforms to support scalable machine learning training workflows model experimentation and enterprise AI deployments. Built containerized AI model deployment pipelines using Docker and Flask based microservices enabling seamless integration of predictive models and inference APIs within enterprise healthcare applications. Designed secure architecture patterns and private cloud networking solutions to safely expose machine learning inference endpoints ensuring HIPAA compliant access to sensitive healthcare analytics services. Implemented enterprise monitoring and observability frameworks using AWS CloudWatch GCP Monitoring Amazon QuickSight and Power BI to track model performance latency prediction accuracy and data drift across production AI systems. Optimized Snowflake based healthcare data warehouses by designing shared dimension schemas and analytical data models enabling efficient ad hoc querying reporting and cross domain healthcare analytics. Developed multistage machine learning inference pipelines using workflow orchestration tools to combine multiple AI models rule engines and contextual reasoning systems for intelligent healthcare recommendations and automated insights. Collaborated with cross functional teams including healthcare analyst data engineer s product managers and compliance teams to design AI driven solutions that improve patient outcomes operational efficiency and regulatory compliance across CVS Health platforms. Environment: SDLC; Python; Scikitlearn; NumPy; SciPy; Matplotlib; Pandas; AWS (S3, DynamoDB, Lambda, EC2, SageMaker, EMR, Redshift); GCP Vertex AI; Snowflake; OLAP/OLTP; Naive Bayes; Random Forest; KMeans; KNN; PCA; Py Spark; XGBoost; TensorFlow; Keras; Power BI. Client: Shell, Houston, TX | Apr 2017 to Feb 2021 Role: Data Scientist / ML Engineer Responsibilities: Collaborated with cross-functional teams in an Agile environment to deliver data science solutions and enterprise analytics platforms supporting workforce performance and operational insights. Performed exploratory data analysis, univariate and multivariate statistical analysis, and time series forecasting to identify workforce trends, demand patterns, and operational performance indicators. Developed machine learning models using Python libraries such as Pandas, NumPy, Scikit-learn, and SciPy including XGBoost, Random Forest, and Support Vector Machines to predict performance outcomes and detect operational risks. Built scalable data pipelines and ETL workflows integrating enterprise datasets from AWS S3, RDS, and Snowflake to support machine learning model training and analytics reporting. Designed and implemented star and snowflake schema data models to support both OLTP and OLAP systems for enterprise analytics and reporting environments. Developed RESTful APIs and Python backend services to expose machine learning models and analytics insights for enterprise web applications. Implemented containerized machine learning workflows using Docker and deployed models using AWS SageMaker to streamline model training, packaging, and deployment processes. Orchestrated distributed workloads using Kubernetes to efficiently manage compute resources and support scalable ML training and inference pipelines. Developed NLP based solutions using NLTK and Scikit-learn for sentiment analysis and text classification of workforce feedback and operational documentation. Designed hybrid data retrieval solutions combining advanced SQL queries with semantic search techniques to improve enterprise knowledge discovery and information retrieval. Built automated data ingestion and transformation pipelines to ensure high data quality, integrity, and timely availability of analytics datasets. Developed interactive dashboards using Power BI and Power Query to visualize workforce performance metrics, operational KPIs, and model insights for business stakeholders. Environment: Python, Pandas, NumPy, Scikit-learn, SciPy, NLTK, XGBoost, Random Forest, SVM, SQL, PL/SQL, Oracle, Teradata, Snowflake, AWS (S3, RDS, SageMaker), Docker, Kubernetes, ETL, OLAP/OLTP, Power BI, Tableau, Power Query, APIs. Client: Resido, Melville, NY | Sep 2016 to Mar 2017 Role: Junior Data Scientist / ML Engineer Responsibilities: Performed exploratory data analysis and data preparation using Python libraries such as Pandas, NumPy, and SciPy to support customer segmentation and market analytics initiatives. Developed and cleaned large datasets using Python, ensuring high data quality and consistency for downstream machine learning and analytics applications. Built ETL pipelines using Python, Alteryx, and Snowflake to integrate and standardize enterprise datasets from multiple distributed data sources. Collected and processed large volumes of structured and unstructured data through web scraping and data wrangling techniques from more than 40 external and internal data sources. Implemented automated data processing workflows using Python, Hadoop, Mahout, and MongoDB to support scalable data ingestion and transformation pipelines. Developed machine learning models using Scikit-learn for customer segmentation, behavioral analysis, and predictive scoring to support targeted business strategies. Performed statistical analysis and multivariate data validation to identify data quality issues and ensure reliability of enterprise datasets. Designed and deployed reporting solutions using Python APIs and Tableau dashboards to provide business stakeholders with actionable insights and performance metrics. Monitored data pipelines and collaborated with data engineering and quality assurance teams to ensure reliability, scalability, and performance of data systems. Translated analytical insights into business recommendations while ensuring compliance with governance and regulatory standards. Environment: Python, Pandas, NumPy, SciPy, Seaborn, Matplotlib, Scikit-learn, NLTK, SQL, Snowflake, Alteryx, Hadoop, MongoDB, ETL, Tableau, APIs, OLTP/OLAP, Oracle, SQL Server. Client: Paychex, Rochester, NY | Jan 2014 to Aug 2016 Role: Data Analyst Responsibilities: Developed and optimized 200+ stored procedures, database views, and SQL queries in MS SQL Server to support payroll, tax, compliance, and financial reporting systems. Collected, validated, and integrated high-volume sales and operational data from more than 40 internal and external sources to support business analytics and reporting initiatives. Designed and delivered interactive Tableau dashboards and KPI reports that enabled business stakeholders to monitor sales performance, compliance metrics, and operational trends. Automated data processing workflows using Python and SQL to extract, clean, and standardize large datasets, reducing manual reporting effort and improving data consistency. Implemented web scraping solutions using Python to collect external datasets and enrich internal analytics systems for improved data coverage and insights. Developed API-based reporting integrations using Python to enable real-time data access and automated updates for Tableau dashboards. Performed data validation and quality checks to ensure accuracy and reliability of enterprise datasets used in financial and compliance reporting. Collaborated with business analysts, database administrators, and reporting teams to deliver scalable data solutions and improve data accessibility across departments. Environment: MS SQL Server, T-SQL, Python, Tableau, Advanced Excel, SQL Server, Oracle, Web Scraping, ETL Automation, APIs, Basic JavaScript. EDUCATION Master s in Information Technology and Project Management, Cumberland University, TN, USA. Aug 2012 - Dec 2013 Bachelor of Technology in Computer Science Engineering, MRCET. Aug 2008 - July 2012 Keywords: cprogramm cplusplus continuous integration continuous deployment artificial intelligence machine learning javascript business intelligence sthree database active directory rlang golang trade national microsoft mississippi procedural language Connecticut New York Tennessee Texas