Python Developer - AI/ML

Meet Patel

Python Developer - AI/ML **********@*****.*** 505-***-**** US Citizen

PROFESSIONAL SUMMARY:

Python Developer – AI/ML with 9+ years of experience designing and deploying scalable web and AI/ML solutions for enterprise and cloud environments.

Expertise in FastAPI, Flask, Django, RESTful APIs, and microservices, delivering high-performance backend systems.

Developed predictive models, NLP pipelines, and embedding-based semantic search solutions using Scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers to improve analytics accuracy and efficiency.

Skilled in data preprocessing, feature engineering, and EDA with relational (MySQL, PostgreSQL) and NoSQL (MongoDB, FAISS) databases.

Proficient in AWS cloud services (EC2, S3, RDS, SageMaker), Docker, MLflow, and CI/CD pipelines to ensure reproducible, scalable, and secure AI/ML deployments.

Experienced in model evaluation, tuning, monitoring, and drift detection to maintain production-quality ML solutions.

Worked with Azure cloud services for deploying and monitoring backend and AI/ML applications, including virtual machines, storage, and managed services.

Strong foundation in testing, code reviews, version control, Agile/Scrum, and SDLC best practices.

Committed to delivering secure, robust applications using OAuth 2.0 and other standards.

Good at problem-solving and communication skills with a proven ability to successfully deliver complex projects within deadlines.

TECHNICAL SKILLS:

Programming & Development: Python (3.x), FastAPI, Flask, Django, RESTful APIs, Microservices Architecture, Celery, Redis

Machine Learning & AI: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, LLM Integration, Prompt Engineering, Regression, Classification, Clustering, Predictive Modeling, Feature Engineering, NLP, Text Classification, Summarization, Embedding Generation, Semantic Search, Retrieval-Augmented Generation (RAG), Model Evaluation & Tuning, Model Monitoring & Drift Detection

Data Science & Databases: Pandas, NumPy, SQL (MySQL, PostgreSQL), MongoDB, Vector Databases (FAISS), Data Cleaning, Data Preprocessing, Data Normalization, Exploratory Data Analysis (EDA)

Cloud & MLOps: AWS EC2, S3, RDS, SageMaker, Docker, MLflow, Joblib, Pickle, Model Serialization & Versioning, ML Pipelines, CI/CD (Jenkins), Reproducibility

Testing, Security & Collaboration: Pytest, Unit & Integration Testing, Code Reviews, OAuth 2.0, Git, Jira, Confluence, Agile/Scrum, SDLC

EXPERIENCE:

Guide house Inc., Remote (US) Client: SBA (Federal Gov.) May 2022 - Present

Python Developer - AI/ML

Developed and enhanced AI/ML-driven Python applications for predictive analytics and automation, improving decision-making efficiency and reducing manual processing time.

Built, trained, and optimized machine learning models using Scikit-learn, enhancing prediction accuracy by selecting algorithms aligned with data patterns and business requirements.

Deployed and maintained AI workloads on AWS (EC2, S3, SageMaker), achieving scalable and high-performing production systems while reducing operational costs.

Designed and implemented PyTorch-based deep learning models for NLP and pattern recognition, achieving improved model performance on real-world datasets.

Applied advanced feature engineering, data preprocessing, and normalization techniques, improving model consistency and reducing training errors.

Integrated LLM solutions via external APIs and applied prompt engineering, enhancing semantic search relevance and response quality.

Extracted, transformed, and analyzed structured datasets from MySQL to support model training, improving data pipeline efficiency and reliability.

Implemented FAISS vector databases and embeddings to enable semantic search and retrieval, reducing query response time.

Monitored model performance, drift, and prediction quality, initiating retraining and optimization cycles to maintain high accuracy and reliability in dynamic datasets.

Implemented comprehensive logging, robust error handling, and monitoring frameworks to support stable production-grade AI systems.

Fine-tuned and deployed Hugging Face Transformer models for text classification, summarization, and embedding generation, improving NLP model outputs and retrieval tasks.

Developed RESTful APIs with FastAPI to serve ML models for real-time and batch inference, enabling faster integration with client applications.

Implemented model serialization and versioning via Joblib and MLflow, ensuring reproducibility, controlled releases, and consistent performance across environments.

Designed and maintained end-to-end ML pipelines for data ingestion, model training, evaluation, and deployment, streamlining the full lifecycle and improving delivery speed.

Containerized AI/ML applications with Docker, ensuring environment consistency and reducing deployment-related errors.

Partnered with data engineers, backend developers, and product teams to translate business requirements into actionable AI/ML solutions, improving feature delivery and solution alignment.

Maintained code quality through Git version control, structured code reviews, thorough documentation, and adherence to best practices, reducing production bugs.

Actively participated in Agile/Scrum ceremonies, contributing to sprint planning, estimation, and iterative delivery of AI features, ensuring timely and high-quality releases.

Tools & Technologies: Python, Scikit-learn, PyTorch, FastAPI, RESTful APIs, SQL (MySQL), FAISS, Joblib, MLflow, Docker, AWS, Machine Learning, Deep Learning, NLP, Agile/Scrum, Git.

Buxton, Fort Worth, TX Dec 2020 – May 2022

Python Developer- Data Science

Developed and maintained Python applications for analytics and ML workflows, improving data processing efficiency and enabling faster business insights.

Performed data cleaning, preprocessing, and feature engineering with Pandas and NumPy, improving dataset quality and preparing reliable inputs for ML model training.

Conducted EDA to identify trends, patterns, and anomalies, delivering actionable insights to stakeholders that informed key business decisions.

Built and optimized Scikit-learn models for regression, classification, and clustering, enhancing model reliability and supporting effective business applications.

Evaluated and fine-tuned ML models using performance metrics (accuracy, F1-score, RMSE), improving predictive reliability and reducing error rates.

Developed lightweight TensorFlow-based neural network models for specific use cases, improving task-specific accuracy and reducing training time.

Built RESTful FastAPI endpoints for serving ML models, enabling seamless integration with backend systems and reducing client request latency.

Serialized and versioned ML models using Pickle, ensuring reproducibility, controlled deployment, and consistent model performance across environments.

Extracted, transformed, and analyzed large datasets from PostgreSQL and MongoDB, improving data accessibility for model training and analytics pipelines.

Deployed ML solutions using Docker and AWS (EC2, S3, SageMaker), improving reproducibility, scalability, and deployment efficiency.

Monitored ML models and executed retraining cycles, maintaining accuracy and mitigating model drift over evolving datasets.

Developed visualizations and reports using Matplotlib and Plotly, effectively communicating insights to technical and non-technical stakeholders, facilitating informed decision-making.

Collaborated in Agile/Scrum teams, participating in sprint planning, reviews, and knowledge-sharing, enhancing team productivity and project delivery.

Maintained high-quality code via Git version control, code reviews, and Jupyter Notebook documentation, reducing defects and improving team collaboration.

Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, TensorFlow, FastAPI, RESTful APIs, Pickle, PostgreSQL, MongoDB, Docker, AWS, Matplotlib, Plotly, Git, Jupyter Notebooks, Agile/Scrum

Tacton, Chicago, IL Feb 2018 - Nov 2020

Python Developer

Developed and maintained backend services using Python 3.x, building scalable and reusable components to support business-critical applications.

Designed and implemented RESTful APIs using Flask, ensuring secure and efficient data exchange across microservices.

Built and optimized database layers using MySQL and MongoDB, leveraging ORM frameworks such as SQLAlchemy for efficient data access.

Integrated asynchronous processing using Celery and Redis to handle background jobs, notifications, and high-volume data processing.

Containerized applications using Docker and supported deployments on AWS (EC2, S3, RDS, IAM) across development and staging environments.

Applied microservices architecture principles to decouple application components, improving system scalability and deployment flexibility.

Integrated OAuth 2.0 protocols to enforce authentication and authorization, strengthening security and controlling user access.

Collaborated with DevOps teams to support CI/CD pipelines using Jenkins and improved release reliability and deployment speed.

Worked in Agile/Scrum teams, participating in sprint planning, daily stand-ups, and retrospectives using Jira and Confluence.

Implemented unit and integration tests using Pytest, reducing production defects and improving code maintainability.

Performed code reviews, refactoring, and performance tuning to improve application efficiency and maintain coding standards.

Monitored application logs and metrics, assisting in debugging and production support in Linux-based environments.

Tools & Technologies: Python 3.x, Flask, RESTful APIs, MySQL, MongoDB, SQLAlchemy, Celery, Redis, Docker, AWS, Microservices Architecture, OAuth 2.0, Jenkins, Jira, Confluence, Pytest.

Logistic InfoTech, India Sep 2016 – Dec 2017

Software Developer

Designed and developed scalable web applications using Python and Django, following the MVT architecture to deliver maintainable and high-performance solutions.

Built and maintained RESTful APIs using Django REST Framework and enabled secure data exchange between backend services and frontend applications.

Implemented user authentication and authorization using Django's built-in security features, including role-based access control and session management.

Developed dynamic user interfaces using HTML5, CSS3, JavaScript, Bootstrap, and jQuery, ensuring cross-browser compatibility and responsive design.

Designed and optimized relational databases using PostgreSQL, wrote efficient queries, and managed schema changes through Django ORM.

Deployed applications on Linux-based servers, configuring Apache for production environments.

Used Git for version control and collaborated with cross-functional teams through code reviews and branching strategies.

Performed unit testing and debugging to identify and resolve application defects and improved overall system stability and reliability.

Participated in Agile/Scrum ceremonies, working closely with product owners and QA teams to deliver features within sprint timelines.

Assisted in basic AWS deployments (EC2, S3) and environment configuration to support development and testing needs.

Followed SDLC best practices, contributing to requirement analysis, development, testing, and production support.

Tools & Technologies: Python, Django, Django REST Framework, HTML5, CSS3, JavaScript, Bootstrap, jQuery, PostgreSQL, Apache, Linux, Git, AWS (EC2, S3), Agile/Scrum, SDLC.

EDUCATION:

Bachelor’s Degree, H.N.G. University, India

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