Sr. Python AI/ML Engineer

Sr. Python Developer AI/ML

Name: Geethanvitha Ganji

Mail Id: *.**********@*****.***

Contact No: 636-***-****

LinkedIn: https://www.linkedin.com/in/geethanvithaganji/ Professional Summary:

• 10+ years of professional experience in software development, including Python, AI/ML, backend development, Full Stack development, REST APIs, databases, and AWS Cloud, with recent experience focused on AI/ML engineering.

• Senior-level experience developing and supporting production applications using Python, with recent hands-on work in AI/ML, NLP, LLMs, RAG, machine learning pipelines, and cloud-based services.

• Strong experience building AI/ML solutions for banking and healthcare environments, including document processing, classification, information extraction, text analysis, model inference, and enterprise knowledge retrieval.

• Hands-on experience developing machine learning pipelines covering data preparation, feature engineering, model training, validation, evaluation, and deployment using Scikit-learn, PyTorch, Pandas, NumPy, and PySpark.

• Developed NLP applications for processing structured and unstructured content, including customer communications, financial documents, clinical notes, healthcare records, and operational information.

• Experience implementing LLM and Generative AI capabilities using Amazon Bedrock, LangChain, embedding models, retrieval workflows, prompt templates, document loaders, and response processing.

• Built RAG applications that combine enterprise documents, vector search, embeddings, and language models to support context-aware question answering and knowledge retrieval.

• Strong backend development experience with FastAPI, Flask, and Django, developing REST APIs, service- layer components, authentication, validation, exception handling, asynchronous processing, and database integrations.

• Developed Python-based data ingestion and transformation workflows using Pandas, NumPy, PySpark, SQL, and AWS S3 to prepare application and machine learning datasets.

• Hands-on experience with AWS Cloud, including S3, SageMaker, Bedrock, Lambda, ECS, EKS, IAM, and CloudWatch for application deployment, ML workloads, data storage, monitoring, and service integration.

• Full Stack development experience using React, JavaScript, TypeScript, HTML5, and CSS3, integrating frontend applications with Python-based REST APIs and backend services.

• Strong database experience with PostgreSQL, MySQL, SQL queries, database connectivity, data validation, indexing, joins, and troubleshooting application-level data issues.

• Experience securing enterprise applications and APIs using OAuth 2.0, JWT, role-based access controls, and controlled access patterns for business and sensitive application data.

• Practical experience with Docker, Kubernetes, AWS ECS/EKS, Jenkins, and GitHub Actions for containerized deployments, CI/CD automation, application releases, and environment management.

• Experienced in production troubleshooting, application monitoring, logging, defect resolution, code reviews, automated testing, and release support using PyTest, unittest, AWS CloudWatch, Splunk, Git, Jira, and Postman.

• Worked closely with business analysts, data scientists, data engineers, QA engineers, application developers, and business stakeholders in Agile/Scrum teams to understand requirements, develop solutions, resolve production issues, and deliver application and AI/ML enhancements. Technical Skills:

Programming

Languages

Python, SQL, JavaScript, TypeScript, HTML5, CSS3

AI / Machine

Learning

Machine Learning, NLP, LLMs, Generative AI, RAG, Scikit-learn, PyTorch, Feature Engineering, Model Training, Model Validation, Model Evaluation, Inference Generative AI / LLM

Amazon Bedrock, LangChain, Embeddings, Vector Search, Prompt Templates, Document Loaders, Retrievers, Question Answering, Text Summarization, Information Extraction

Python Frameworks FastAPI, Django, Flask, SQLAlchemy Data Processing Pandas, NumPy, PySpark, Data Transformation, Data Validation, ETL/Data Processing, OCR

Frontend React, JavaScript, TypeScript, HTML5, CSS3 API Development REST APIs, FastAPI, Flask, JSON, XML, HTTP, API Validation, API Integration, Postman Professional Summary:

Client: Santander - Boston, MA.

Apr 2025 – Till Date

Role: Sr. Python Developer AI/ML

Responsibilities:

• Designed and developed production-grade Python services for AI/ML applications supporting banking workflows, data processing, and intelligent automation.

• Built and maintained machine learning pipelines for data preparation, feature engineering, model training, validation, and deployment using Python, Scikit-learn, and AWS services.

• Developed NLP solutions to extract, classify, and analyze information from customer communications, financial documents, and operational records.

• Implemented LLM-based capabilities for document understanding, text summarization, question answering, and knowledge retrieval across internal banking content.

• Developed RAG workflows using LangChain, embedding models, and vector search to provide context- aware responses from approved enterprise data sources.

• Integrated Amazon Bedrock with Python applications to work with foundation models while maintaining controlled prompts, response handling, and enterprise data access patterns.

• Built document-processing workflows using Python, OCR, NLP techniques, and validation rules to convert unstructured banking documents into structured information.

• Developed reusable LangChain components for prompt templates, document loaders, retrievers, model interaction, and response post-processing across AI use cases.

• Designed ML model evaluation and validation workflows to compare model outputs, monitor prediction behavior, and identify data or model issues before production releases.

• Implemented feature engineering and data transformation logic using Pandas, NumPy, and SQL for preparing structured datasets used by machine learning models.

• Developed intelligent classification and entity-extraction workflows for financial and customer-related data using NLP, Python, and supervised machine learning techniques.

• Created data ingestion and preprocessing pipelines that consumed information from relational databases, APIs, files, and cloud storage using Python and AWS S3.

• Integrated AI/ML services with enterprise applications through REST APIs built with FastAPI, enabling downstream banking applications to consume model and LLM capabilities.

• Developed backend components with FastAPI for authentication-aware API access, request validation, asynchronous processing, exception handling, and integration with AI services.

• Containerized Python-based AI/ML services using Docker and deployed workloads through AWS ECS/EKS, supporting consistent application environments across development and production.

• Used AWS Lambda, S3, CloudWatch, IAM, and related AWS services to build and operate cloud-based components supporting AI/ML workloads.

• Implemented secure API and application integration patterns using OAuth 2.0, JWT-based authorization, and controlled access to enterprise AI services and data sources.

• Developed lightweight React interfaces for selected internal AI applications, connecting frontend workflows with Python FastAPI services and backend ML capabilities. Cloud – AWS

AWS S3, Amazon SageMaker, Amazon Bedrock, AWS Lambda, AWS ECS, AWS EKS, AWS IAM, AWS CloudWatch, Amazon RDS

Databases PostgreSQL, MySQL, SQL, SQLAlchemy, Database Queries, Joins, Indexing Orchestration Docker, Kubernetes, AWS ECS, AWS EKS Security OAuth 2.0, JWT, Role-Based Access Control, API Authentication, Authorization CI/CD & DevOps Jenkins, GitHub Actions, Git, GitLab, Docker, AWS Deployment Processes Testing PyTest, unittest, Integration Testing, API Testing, Regression Testing Monitoring AWS CloudWatch, Splunk, Application Logging, Exception Tracking, Production Troubleshooting

Version Control Git, GitLab, GitHub, Jira, Agile/Scrum, Code Reviews, Release Management Operating Systems Linux, PyCharm, Eclipse, Apache

• Implemented application logging, exception tracking, and operational monitoring using AWS CloudWatch and centralized logging practices to troubleshoot production AI/ML services.

• Built automated testing for Python services and ML components using PyTest, including API validation, data-processing checks, model-service integration, and regression scenarios.

• Established CI/CD workflows using Git, Jenkins/GitHub Actions, Docker, and AWS deployment processes to promote Python and AI/ML services through controlled environments.

• Worked closely with data engineers, application developers, business stakeholders, and QA teams to translate banking requirements into AI/ML solutions, troubleshoot production issues, and support releases.

Environment: Python, FastAPI, Pandas, NumPy, Scikit-learn, PyTorch, NLP, LLMs, RAG, LangChain, Amazon Bedrock, Generative AI, Machine Learning, Model Evaluation, Feature Engineering, REST APIs, React, SQL, AWS S3, AWS Lambda, AWS ECS, AWS EKS, AWS IAM, AWS CloudWatch, Docker, Git, Jenkins, GitHub Actions, PyTest, OAuth 2.0, JWT.

Client: Kaiser Permanente - Oakland, CA.

Aug 2023 – Mar 2025

Role: Python Developer AI/ML

Responsibilities:

• Developed Python services for healthcare data processing, clinical document workflows, and internal application features using FastAPI, Flask, and Django.

• Built machine learning pipelines for healthcare data preparation, feature engineering, model training, validation, and batch inference using Pandas, NumPy, Scikit-learn, and PySpark.

• Implemented NLP workflows to extract relevant information from clinical notes, documents, and unstructured healthcare content using spaCy, NLTK, and transformer-based models.

• Developed document classification and information extraction workflows to organize unstructured healthcare records and make processed information available to downstream applications.

• Worked with data scientists to prepare training datasets, perform feature analysis, evaluate model outputs, and troubleshoot issues affecting model quality and inference results.

• Created reusable ML pipelines for data ingestion, preprocessing, model training, model packaging, and inference using AWS SageMaker and Python-based services.

• Integrated AWS S3, SageMaker, Lambda, and ECS services with Python applications for storing datasets, executing workloads, and serving machine learning functionality.

• Developed RESTful APIs using FastAPI and Flask to expose data-processing and machine learning services to internal healthcare applications.

• Implemented backend business logic with Django and SQLAlchemy, including database access, validation, exception handling, and service-layer components.

• Built Python-based data ingestion jobs to collect information from relational databases, application feeds, and healthcare data sources for downstream analytics and ML processing.

• Used PostgreSQL and SQL to design queries, retrieve application data, troubleshoot data issues, and support reporting and machine learning workflows.

• Developed responsive application components using React and TypeScript, integrating frontend screens with Python REST APIs and backend services.

• Implemented authentication and authorization for APIs using OAuth 2.0, JWT, and role-based access controls to protect application and healthcare-related data.

• Containerized Python applications and ML services using Docker and deployed workloads through AWS ECS and Kubernetes environments.

• Established automated build and deployment workflows using Jenkins, Git, and AWS services for Python APIs, application components, and machine learning services.

• Added application and model monitoring through structured logging and operational dashboards using CloudWatch and Splunk, helping teams investigate failed jobs and service issues.

• Optimized Python data-processing routines by improving database queries, managing memory-intensive operations, and restructuring batch workflows for large healthcare datasets.

• Created unit and integration tests using PyTest and unittest, covering API endpoints, data-processing logic, model-serving components, and application workflows.

• Worked with Git and Jira to manage source-code changes, defects, sprint tasks, production issues, and deployment activities across development and release environments.

• Participated in design discussions, code reviews, troubleshooting sessions, release planning, and production support while coordinating with data scientists, QA engineers, business analysts, and application teams.

Environment: Python, FastAPI, Flask, Django, Pandas, NumPy, Scikit-learn, PySpark, spaCy, NLTK, NLP, Machine Learning, AWS SageMaker, AWS S3, AWS Lambda, AWS ECS, Kubernetes, Docker, PostgreSQL, SQLAlchemy, SQL, React, TypeScript, REST APIs, OAuth 2.0, JWT, Jenkins, Git, GitHub, PyTest, unittest, Splunk, AWS CloudWatch, Jira, Agile/Scrum.

Client: Gap Inc. - San Francisco, CA.

Jun 2022 – Mar 2023

Role: Python Full Stack Developer/ML

Responsibilities:

• Implemented scheduled Python jobs for data preparation and model-related processing using AWS Lambda and Amazon S3.

• Developed backend services using Python, Django, and FastAPI for retail application modules handling product, inventory, customer, and order-related workflows.

• Built and maintained REST APIs for communication between Python services, frontend applications, and downstream retail systems.

• Developed reusable frontend components using React, JavaScript, HTML5, and CSS3 for product search, catalog views, inventory screens, and operational dashboards.

• Integrated React applications with Python APIs and handled API validation, error responses, pagination, filtering, and request/response data mapping.

• Worked with PostgreSQL and MySQL to design tables, write complex SQL queries, create indexes, and support application-level data retrieval.

• Used Pandas and NumPy to clean, transform, and prepare retail datasets used for analytical and machine learning workflows.

• Developed machine learning pipelines using Scikit-learn for customer and product data analysis, including feature preparation, model training, validation, and inference workflows.

• Prepared training datasets by combining information from product, transaction, inventory, and customer- related sources and addressed missing, inconsistent, and duplicate records.

• Containerized Python services using Docker and supported deployments across AWS environments through established CI/CD pipelines.

• Worked with Jenkins to automate application builds, test execution, packaging, and deployment activities for Python and frontend components.

• Implemented application authentication and authorization using JWT and role-based access controls across protected API endpoints.

• Added unit and integration tests using PyTest and supported API validation through Postman before promoting changes across environments.

• Used Git for source control and participated in branch management, pull requests, code reviews, merge activities, and release preparation.

• Investigated production and lower-environment issues by reviewing AWS CloudWatch logs, application exceptions, API responses, and database queries.

• Collaborated with business analysts, QA engineers, data teams, and other developers to clarify retail application requirements, troubleshoot defects, and deliver sprint-level enhancements.

• Participated in Agile/Scrum ceremonies including sprint planning, backlog discussions, daily stand-ups, defect triage, and release coordination while supporting ongoing application maintenance. Environment: Python, Django, FastAPI, React, JavaScript, HTML5, CSS3, REST API, Pandas, NumPy, Scikit-learn, PyTest, PostgreSQL, MySQL, SQL, AWS, Amazon S3, AWS Lambda, Amazon RDS, CloudWatch, Docker, Jenkins, Git, Postman, JWT, Agile/Scrum

Client: Mavenir – Bengaluru, India.

May 2018 – Nov 2021

Role: Python Developer

Responsibilities:

• Worked with PostgreSQL and MySQL to design tables, write SQL queries, manage application data, and support database-driven business workflows.

• Developed backend services and application modules using Python for telecom-focused applications supporting network operations and service workflows.

• Built and maintained REST APIs using Flask and Django to exchange service, configuration, and operational data between application components.

• Created Python scripts to automate recurring operational tasks, file processing, data validation, and application maintenance activities.

• Integrated backend services with internal applications and third-party systems using REST APIs, JSON, XML, and HTTP-based interfaces.

• Implemented request validation, exception handling, logging, and response handling across Python services to maintain consistent application behavior.

• Developed data-processing routines using Pandas and native Python libraries for parsing, transforming, validating, and loading operational data.

• Worked with Linux environments to deploy applications, review logs, troubleshoot service issues, and perform routine application support activities.

• Used Git for source-code management, branching, merging, code reviews, and maintaining changes across development and release branches.

• Wrote unit and integration tests using PyTest and unittest to validate application modules, API behavior, database operations, and error scenarios.

• Investigated application issues reported during testing and production support, traced errors through logs and code, and implemented fixes based on root-cause findings.

• Developed reusable Python utilities and common modules for database connectivity, API communication, configuration handling, logging, and file operations.

• Worked with JSON, XML, CSV, and other structured data formats while integrating telecom application components and processing service-related information.

• Participated in Agile/Scrum activities including sprint planning, daily stand-ups, backlog discussions, defect reviews, and technical discussions with developers and QA teams.

• Assisted QA teams with test-data preparation, defect analysis, application behavior clarification, and resolution of issues identified during functional and regression testing.

• Supported application releases by preparing deployment changes, validating configuration settings, checking service availability, and addressing post-deployment issues.

• Maintained technical documentation for application modules, API details, database changes, deployment procedures, troubleshooting steps, and recurring support activities. Environment: Python, Flask, Django, REST API, Pandas, PostgreSQL, MySQL, SQL, PyTest, unittest, JSON, XML, Linux, Git, GitLab, Jenkins, Docker, HTML, JavaScript, Agile/Scrum, Jira. Client: Mistral Solutions – Bengaluru, India.

Jan 2015 – Apr 2018

Role: Software Developer

Responsibilities:

• Integrated application components with MySQL databases using SQL queries for inserting, updating, retrieving, and validating records.

• Developed application modules using Python based on functional requirements and technical specifications provided by senior developers.

• Built reusable Python functions and modules for data processing, file handling, validation, and routine application tasks.

• Worked with Django to develop and maintain web application features, forms, views, and basic request- handling components.

• Created and updated REST API endpoints for exchanging application data between internal modules and client applications.

• Wrote SQL queries, joins, and basic stored procedures to support application features and data-related tasks.

• Developed input validation and exception-handling routines to improve application reliability and handle unexpected data conditions.

• Worked with HTML, CSS, and JavaScript to make changes to existing web pages and connect front-end forms with Python-based application logic.

• Used Git for source-code management, maintaining branches and committing application changes during development activities.

• Participated in code reviews with senior developers and incorporated feedback related to coding standards, functionality, and maintainability.

• Created unit and functional test cases using unittest and manually verified application changes before handing builds to the QA team.

• Investigated application defects reported by QA and users, reproduced issues, and made code changes to resolve functional problems.

• Used Linux commands for application setup, log checking, file management, and basic troubleshooting in development and test environments.

• Prepared technical notes and application documentation covering module changes, database updates, configuration details, and deployment instructions.

• Assisted with application deployments across development and test environments and verified basic application functionality after releases.

• Worked closely with business analysts, QA engineers, and senior developers to clarify requirements, troubleshoot issues, and deliver assigned enhancements. Environment: Python, Django, REST APIs, MySQL, SQL, HTML, CSS, JavaScript, Git, Linux, unittest, JSON, XML, Apache, JIRA, Eclipse/PyCharm.

Contact this candidate