Home

manish Reddy Kotha - Sr.python developer with AIML /AI ENGINEER

manishreddyk9390@gmail.com

Location: Angier, North Carolina, USA

Relocation:

Visa: h1-b

Resume file: Sr-Python_developer_with_AIML_-_Manish_reddy_1786039022042.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

Sr. Python Developer with AI/ML Name: Manish Reddy Kotha Mail Id: manishreddyk9390@gmail.com Contact No: 8609648167 Professional Summary: Senior Python Developer with 10+ years of experience designing, developing, and deploying scalable AI/ML, Generative AI, and cloud-native enterprise solutions on AWS for financial and healthcare systems. Hands-on experience building AI/ML and Generative AI solutions using Amazon SageMaker, PyTorch, TensorFlow, Hugging Face Transformers, NLP, predictive modeling, and Retrieval-Augmented Generation (RAG) pipelines. Experience developing Agentic AI and LLM-powered applications using LangChain, LangGraph, Claude, OpenAI API, and Prompt Engineering for semantic search, intelligent automation, and enterprise AI workflows. Skilled in building predictive models for risk scoring, fraud detection, and underwriting use cases, integrated into production data pipelines for financial and insurance systems. Strong expertise in designing scalable Python-based ETL pipelines and enterprise data platforms supporting financial analytics, reporting, and decision intelligence. Proficient in SQL (PostgreSQL, MySQL, SQL Server, Snowflake) and NoSQL (MongoDB) for data transformation, validation, aggregation, and optimization across large-scale datasets. Experience building backend services and APIs using FastAPI, Flask, and Django to support AI-driven and data-driven applications. Hands-on experience deploying AI/ML applications on AWS using SageMaker, Docker, Kubernetes, CI/CD, and cloud-native technologies for scalable enterprise solutions. Strong focus on data governance, security, and regulatory compliance (HIPAA), ensuring secure handling of sensitive financial and healthcare data. Experience supporting deployment, monitoring, MLOps, and LLM lifecycle management using MLflow, Docker, Kubernetes, CI/CD, and model evaluation techniques for scalable production systems. Collaborative team player experienced in Agile/Scrum environments, working closely with data engineers, data scientists, and business stakeholders. Technical Skills Programming Languages/ Backend Python, JavaScript, SQL, HTML/CSS, Asynchronous Programming, Concurrency, Multithreading, Multiprocessing, Memory Management, asyncio AI/ML Frameworks PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, Feature Engineering, Model Training, Hyperparameter Tuning NLP & LLMs Hugging Face Transformers, BERT, GPT, Claude, SpaCy, NLTK, LLM fine-tuning, Retrieval-Augmented Generation (RAG), Prompt Engineering, Text Classification, Entity Extraction, Summarization, Foundation Models, LLM Evaluation MLOps & Deployment MLflow, Airflow, Docker, Kubernetes, CI/CD, Cloud Deployement(AWS), Model Monitoring, Automated Retraining, REST APIs, Microservices, MCP (Model Context Protocol), Async Workflows Cloud Platforms Azure (Blob Storage, Virtual Machines, Functions, Synapse Analytics, Azure ML), GCP, Amazon Bedrock, AWS Lambda Databases PostgreSQL, MySQL, Microsoft SQL Server, Snowflake, MongoDB (Aggregation Pipelines, Indexing Strategies, Schema Design, Data Modeling, NoSQL Queries), Redis Data Engineering PySpark, ETL Development, Data Pipelines, Data Transformation, Data Validation, Batch & Real-time Processing, Data Ingestion, Data Processing Workflows Data Visualization Power BI, Tableau, Matplotlib, Seaborn AI Tools & Agents GitHub Copilot, Claude, AI Agents, LangChain, LangGraph, Generative AI, Semantic Search systems, RAG Frameworks, Agentic AI, Multi-Agent Workflows, Tool Integration, Agent Orchestration, Memory Management, Autonomous Decision-Making DevOps & Version Control Docker, Kubernetes, Git, GitHub, CI/CD (GitLab CI, Jenkins, Azure DevOps) Web & Frontend React.js, Angular, Flask, Django, RESTful APIs Monitoring & Logging ELK Stack, Model Performance Monitoring Security & Compliance HIPAA, Data Governance, Role-Based Access Control, OAuth2, JWT Project Management Agile/Scrum, JIRA, Cross-functional Collaboration Professional Experience: Client: M&T BANK Buffalo Newyork Oct 2023 Tilldate Role: Sr. Python Developer with AI/ML Responsibilities: Designed and implemented scalable Python-based ETL pipelines for ingestion, transformation, validation, and processing of large-scale financial datasets across enterprise systems. Built and optimized data pipelines to support financial analytics, reporting, and downstream data-driven applications, ensuring high data quality and consistency. Developed and executed complex SQL queries for data transformation, validation, aggregation, and reporting across enterprise financial platforms. Implemented data validation and data quality frameworks using Python and SQL to ensure accuracy, integrity, and reliability of financial datasets. Built a Retrieval-Augmented Generation (RAG) pipeline using LangChain and Pinecone/FAISS to enable semantic search over enterprise financial documents, reducing manual document lookup time. Designed prompt engineering workflows and integrated OpenAI API-based LLM capabilities into internal data pipelines to automate document summarization and classification tasks. Leveraged MCP (Model Context Protocol) to connect LLM-based tools with internal enterprise data sources, enabling context-aware AI assistance for backend workflows. Collaborated with data science teams to integrate model outputs into production ETL pipelines, supporting AI-assisted decision-making for financial reporting. Designed and developed Python automation scripts to streamline recurring data processing tasks, reducing manual effort and improving operational efficiency. Troubleshot and resolved data pipeline failures, performance bottlenecks, and production issues, ensuring system stability and minimal downtime. Built high-throughput batch and real-time data processing pipelines to handle large volumes of structured and semi-structured financial data. Developed backend services using FastAPI and Flask to support data-driven applications, API integrations, and enterprise system communication. Designed and implemented data exchange and integration frameworks to enable seamless communication between databases, APIs, and enterprise platforms. Applied concurrency and parallel processing techniques using asyncio, multithreading, and multiprocessing to improve ETL pipeline performance and throughput. Developed automated API and data pipeline testing frameworks using Python (pytest, requests) to validate business logic and ensure reliable system integration. Integrated CI/CD pipelines (GitHub Actions, Jenkins) for continuous testing, deployment, and monitoring of data pipelines and backend services. Implemented enterprise IAM integrations using SSO, OAuth2, RBAC, and Azure AD/Okta for secure authentication and authorization across REST APIs and backend services. Managed Linux-based deployments including service monitoring (systemctl), log analysis, process management, and automation using Bash/Python scripts. Designed and optimized MongoDB data models using schema design, indexing strategies, and aggregation pipelines to improve query performance and support large-scale financial data processing. Designed, developed, and deployed scalable AI/ML and Generative AI solutions on AWS using Amazon Bedrock, SageMaker, AWS Lambda, and cloud-native services for enterprise intelligent automation. Built Agentic AI applications using LangChain and LangGraph by implementing multi-agent workflows, tool integration, orchestration, memory management, and autonomous decision-making capabilities for enterprise AI solutions. Implemented monitoring and observability frameworks to track data pipeline performance, data quality, and system health in production environments. Performed data preprocessing, transformation, and feature engineering across structured and semi-structured financial datasets to support analytics workflows.Collaborated with cross-functional teams including data engineers, analysts, and business stakeholders to deliver scalable and secure enterprise data solutions Environment: Python, FastAPI, Flask, OpenAI API, LangChain, LangGraph, Copilot Frameworks, REST APIs, Microservices, asyncio, Concurrent Processing (Multithreading/Multiprocessing), MCP, Pinecone, FAISS, RAG Pipelines, Docker, Kubernetes, Azure (Azure ML, Azure Functions, Blob Storage), MLflow, CI/CD (GitHub Actions, Jenkins), Git, JIRA. Client: USAA SanAntonio, TX. Jul 2022-Sep2023 Role: Sr. Python Developer with AI/ML Responsibilities: Designed and developed scalable Python-based ETL pipelines to ingest, transform, and process large-scale financial and insurance datasets for analytics and decision-support systems. Built and maintained data pipelines supporting customer analytics, reporting, and enterprise data-driven applications across financial and insurance domains. Developed and optimized SQL queries and transformation logic to validate, aggregate, and process structured and semi-structured data across Snowflake, Redshift, and distributed data platforms. Implemented robust data validation and data quality frameworks using Python and SQL to ensure accuracy, consistency, and reliability of enterprise datasets. Designed and implemented data processing workflows on Azure Blob Storage, Synapse Analytics, and Snowflake to support scalable data ingestion and transformation. Automated data pipeline validation and backend testing workflows using Python, improving system reliability and reducing manual intervention. Built automated API testing solutions using Python (pytest, requests) to validate service integrations, business logic, and data contracts across enterprise systems. Developed LLM-powered solutions using Claude and foundation models, applying prompt engineering, fine-tuning strategies, and model evaluation to improve enterprise AI application performance. Troubleshot and resolved data pipeline failures, data inconsistencies, and performance issues, ensuring stability of production systems. Developed and optimized distributed data processing pipelines using PySpark, improving processing efficiency and enabling scalable handling of large enterprise datasets. Tuned performance across Python, Spark, and database layers by optimizing queries, partitioning strategies, and execution plans to reduce latency and improve throughput. Designed large-scale test datasets and validation strategies to ensure data accuracy and reliability across financial analytics and risk systems. Integrated CI/CD pipelines to support continuous testing, deployment, and monitoring of data pipelines and backend services. Implemented workflow orchestration using Airflow to manage scheduling, execution, and monitoring of batch data processing pipelines. Applied PyTorch and Hugging Face Transformers to build NLP models for automated classification of customer service and insurance text data. Built a RAG-based retrieval system using Pinecone/FAISS to support faster, context-aware search across large insurance and financial document repositories. Fine-tuned pretrained transformer models for domain-specific entity extraction and text classification tasks, improving downstream analytics accuracy. Integrated LLM-based summarization into reporting pipelines, reducing manual analyst review time for large document sets. Applied feature engineering, data preprocessing, and transformation techniques across structured and semi-structured datasets to support analytics workflows. Developed Python automation scripts to streamline recurring data processing tasks and improve operational efficiency across enterprise systems. Environment: Python, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, RAG, LLMs, NLP, MLflow, Docker, Kubernetes, Azure (Blob Storage, Virtual Machines, Azure Functions, Synapse Analytics), Snowflake, Pinecone, FAISS, Power BI, Tableau, Git, Jira, CI/CD pipelines, Airflow. Client: HCA Healthcare Nashville, TN. Aug 2021 jun 2022 Role: Python Developer with ML Responsibilities: Built and maintained Python-based data pipelines and retrieval systems to support enterprise knowledge platforms, customer support analytics, and data-driven decision-making applications. Developed NLP and data processing pipelines using Python and transformer frameworks to extract insights, summarize documents, and process large-scale financial and insurance datasets. Designed and implemented predictive models integrated with enterprise data pipelines for risk scoring, loan approvals, underwriting, and fraud detection using Python-based frameworks. Evaluated and tuned machine learning models (scikit-learn, XGBoost) using cross-validation and hyperparameter optimization to improve prediction accuracy for risk scoring and fraud detection models. Architected data-driven recommendation and personalization systems leveraging structured datasets to enhance customer engagement and improve financial and insurance business workflows. Implemented scalable ETL/ELT pipelines and data processing workflows using Azure Blob Storage, Synapse Analytics, and Snowflake for enterprise data management. Developed Python-based automation frameworks to validate ETL pipelines, backend services, and data processing workflows across large-scale enterprise datasets. Created SQL-driven validation and transformation layers to ensure data accuracy, consistency, and integrity across Snowflake, Redshift, and relational database systems. Built automated API testing frameworks using Python to validate service integrations, data contracts, and business logic in financial and insurance systems. Integrated automated data pipeline validation workflows into CI/CD pipelines, enabling continuous testing and faster delivery of enterprise data solutions. Integrated advanced data retrieval solutions including vector databases to enhance search performance and enable efficient access to enterprise data repositories. Implemented automated data pipeline orchestration workflows using Airflow, enabling scheduled processing, monitoring, and efficient handling of evolving data patterns. Deployed scalable backend services and data processing systems using Docker, Kubernetes, and cloud infrastructure to ensure high availability and operational efficiency. Leveraged advanced data processing techniques and NLP models to support document classification, entity extraction, and large-scale text analytics within enterprise systems. Implemented large-scale data processing and feature engineering workflows using Databricks and distributed systems to support high-volume data analytics and reporting. Environment: Python, scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, Seaborn, NLTK, spaCy, SQL Server, MongoDB, Airflow, MLflow, Docker, Kubernetes, FastAPI, Flask, Tableau, Power BI, HIPAA-compliant frameworks. Client: Amway New Delhi,India Nov2016 Nov 2019 Role: Python Full Stack Developer Responsibilities: Developed Python-based Django and Flask backend modules for business-critical capabilities including order management, payment processing, inventory workflows, and reporting dashboards. Integrated RESTful APIs and third-party services for inventory tracking, CRM connectivity, logistics operations, and enterprise data exchange across business platforms. Built dynamic, responsive frontend interfaces using React.js to enhance usability, improve customer interactions, and reduce page load times. Implemented unit and integration testing using PyTest and Jest to improve release quality, reduce regression issues, and strengthen application reliability. Designed scalable backend microservices using Python and REST APIs to support high-volume e-commerce transaction processing and downstream system integrations. Optimized PostgreSQL and MySQL queries, schema design, and data access patterns to improve application performance and reporting efficiency. Monitored and supported applications on Azure cloud infrastructure including Virtual Machines, Blob Storage, Azure SQL Database, and Azure Functions. Collaborated with cross-functional teams in Scrum environments, performed code reviews, and supported CI/CD deployments through GitLab pipelines. Created dashboards and reporting tools using Tableau and Matplotlib to support business analytics, KPI tracking, and operational decision-making. Implemented role-based access control and strengthened application security using JWT and OAuth2 authentication and authorization mechanisms. Automated ETL workflows using Python, Pandas, and Airflow to extract, transform, and load operational data, reducing manual effort and improving data consistency. Environment: Python, Django, Flask, React.js, PostgreSQL, MySQL, Azure (Virtual Machines, Blob Storage, Azure SQL Database, Azure Functions), REST APIs, Git, GitLab, PyTest, Jest, Tableau, Pandas, Airflow, Agile/Scrum, JWT, OAuth2, CI/CD. Client: Birlasoft Pune, India. Aug2013 Oct 2016 Role: Python Full Stack Developer Responsibilities: Designed and optimized RESTful APIs using Python, Flask, and Django to support enterprise web applications and service-oriented backend architectures. Developed responsive web interfaces using React.js and Angular to integrate frontend components with backend services using AJAX and WebSockets to enable seamless data flow, real-time updates, consistent user interactions. Implemented authentication and authorization mechanisms using OAuth2 and JWT to strengthen application security and access control. Performed database design, optimization, and query tuning across PostgreSQL, MySQL, and MongoDB to improve data retrieval performance and transactional efficiency. Automated deployment and environment provisioning using Docker and CI/CD pipelines including Jenkins and GitLab CI to streamline build and release workflows. Applied unit and integration testing using PyTest and Selenium to improve code quality, reduce production defects, and support stable releases. Migrated legacy modules to modern Python frameworks while preserving backward compatibility, minimizing downtime, and improving maintainability. Collaborated with cross-functional teams in Agile environments, contributing to sprint planning, code reviews, estimation, and daily engineering discussions. Monitored application performance using New Relic and ELK Stack to identify bottlenecks, improve stability, and proactively resolve runtime issues. Implemented structured logging, exception handling, and error management patterns to improve traceability, supportability, and long-term maintainability. Integrated third-party APIs including payment gateways, messaging services, and analytics platforms to extend application capabilities and business workflows. Managed version control, branching strategies, and collaboration workflows using Git and GitHub to support coordinated development and release cycles. Performed data validation and ETL processing across multiple applications to maintain consistency between relational and NoSQL data stores. Environment: Python (Flask, Django), RESTful APIs, Microservices, React.js, Angular, HTML5, CSS3, JavaScript, AJAX, PostgreSQL, MySQL, MongoDB, Docker, Jenkins, GitLab CI/CD, Azure DevOps, PyTest, Selenium, ELK Stack, New Relic, Git, GitHub, Agile, Scrum, JIRA. Keywords: continuous integration continuous deployment artificial intelligence machine learning javascript business intelligence active directory trade national Idaho Tennessee Texas