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Dharani Thakkallapally - Python full stack with AI/ML

[email protected]

Location: Dallas, Texas, USA

Relocation: yes

Visa: H1B

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DHARANI THAKKALLAPALLY Senior Python Developer - AI/ML Solutions +1 (816) 269-6717 | [email protected] | linkedin.com/in/t-dharani-683382151 EXECUTIVE PROFESSIONAL SUMMARY Result-driven, highly visionary Senior Python Developer and AI/ML Engineer with over 10+ years of enterprise-level software engineering experience. Recognized expert in designing, architecting, developing, and deploying highly scalable backend systems, low-latency microservices, and advanced, production-grade Artificial Intelligence (AI), Machine Learning (ML), and Generative AI workflows. Proven capability across the complete software development lifecycle (SDLC) and ML lifecycle (MLOps) spanning high-throughput feature pipeline design, real-time advanced natural language processing (NLP), robust computer vision pipelines, time-series predictive planning, cloud-native virtualization, and continuous integration and continuous delivery (CI/CD) automation. Exceptional technical mastery anchored deeply in Python (Django, Flask, FastAPI, AsyncIO) with extensive expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Demonstrated engineering breakthroughs in the integration of state-of-the-art Large Language Models (LLMs including GPT-4o, Claude 3.5 Sonnet, Llama 3, Mixtral) with orchestration tools like LangChain and LlamaIndex to design and scale intelligent Retrieval-Augmented Generation (RAG) engines, agentic automated workflows, and complex semantic search frameworks. Champion of cloud-native, multi-region high-availability infrastructure utilizing Amazon Web Services (AWS) and Microsoft Azure, backed by immutable Infrastructure as Code (Terraform), robust containerization (Docker, Kubernetes), and modern distributed messaging and database solutions (PostgreSQL, MongoDB, Cassandra, Apache Kafka, Redis, Apache Spark). A consummate leader and architectural advisor who excels at bridging complex domain requirements with high-performing, clean, testable code bases adhering strictly to Object-Oriented Programming (OOP) principles, Functional Programming, Domain-Driven Design (DDD), and rigorous OWASP-compliant application security standards. Documented track record of driving monumental digital transformation strategies within Fortune 500 enterprises across Financial Services, Healthcare logistics, and Aviation operations, while consistently fostering an agile culture of continuous improvement, mentorship, test-driven development (TDD), and meticulous model explainability and transparency. CORE TECHNICAL SKILLS MATRIX Backend Engineering & Core Python Python (Expert), AsyncIO, Django, Flask, FastAPI, Node.js, Express.js, Multiprocessing, Concurrency, Jinja2, RESTful API Architecture, GraphQL, WebSockets, gRPC, Celery, Event Loops, Object-Oriented Design (OOD). AI/ML Pipelines & Model Deployment MLflow, Kubeflow, BentoML, Triton Inference Server, Weights & Biases, Airflow, Apache Spark ML, Seldon Core, ONNX Runtime, Feature Stores (Feast), Model Versioning, A/B Testing Frameworks, Shadow Deployment, Canary Releases, Real-Time Inference APIs, Batch Prediction Pipelines, Model Monitoring & Drift Detection. Artificial Intelligence & Machine Learning TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Transformers, Generative Adversarial Networks (GANs), XGBoost, LightGBM, Random Forests, Reinforcement Learning (Q-learning). Natural Language Processing & GenAI Natural Language Toolkit (NLTK), SpaCy, BERT, RoBERTa, Text Classification, Named Entity Recognition (NER), Sentiment Analysis, Coreference Resolution, GPT-4o, Claude 3/3.5, Gemini Pro, Llama 3, Mixtral, Hugging Face Transformers, LangChain, LlamaIndex, AutoRAG, Semantic Search, Vector Embeddings, Custom Prompt Engineering. Statistical Modeling & MLOps ARIMA, Prophet, Time-Series Analysis, Anomaly Detection, Isolation Forests, Optuna, GridSearchCV, RandomSearchCV, Hyperparameter Tuning, Advanced Feature Engineering, Model Interpretability & Explainability (SHAP, LIME), MLflow, Airflow, BentoML, Kubeflow, Weights & Biases, Triton Inference Server. Cloud Infrastructure & DevOps AWS (EC2, Lambda, S3, SageMaker, Glue, RDS, DynamoDB, ECS, EKS, IAM, CloudFormation), Microsoft Azure (Azure ML, Synapse Analytics, Data Factory, Cognitive Services, App Services, Azure Functions, Blob Storage, CosmosDB), Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, GitLab CI, Azure DevOps, SonarQube, Helm Charts. Data Engineering & Messaging Systems PostgreSQL, MySQL, MongoDB, DynamoDB, Cassandra, Snowflake, Apache Spark, Apache Hadoop, MapReduce, Apache Kafka, RabbitMQ, Redis Cache, Hazelcast, SQLAlchemy ORM, Django ORM, ETL Pipelines, Data Lakehouse Architectures, Delta Lake, Presto. Testing, Security & Collaboration Pytest, UnitTest, Mocha, Chai, Selenium Web Driver, Cypress, SonarQube Linting, JWT (JSON Web Tokens), OAuth 2.0, SAML, SSL/TLS, Postman, Swagger UI, OpenAPI Specifications, Git, GitHub Enterprise, JIRA, Confluence, Agile/Scrum Methodologies. CHRONOLOGICAL PROFESSIONAL EXPERIENCE Senior AI/ML Engineer | Freddie Mac McLean, VA February 2025 Present Architect, design, and pilot scalable enterprise-grade AI/ML software platforms utilizing Python, TensorFlow, and PyTorch, setting up state-of-the-art machine learning production pipelines across supervised, unsupervised, and deep reinforcement learning paradigms to manage multi-billion dollar mortgage portfolios. Formulate high-throughput, ultralow-latency asynchronous RESTful and GraphQL APIs via FastAPI and Flask to serve real-time model inferences under heavy traffic spikes, successfully securing all endpoints with OAuth2, JWT, and multi-factor hardware security tokens. Implement core enterprise NLP infrastructures leveraging SpaCy, NLTK, and custom-tuned BERT/RoBERTa transformer architectures for high-speed automated document classification, structural Named Entity Recognition (NER), and contextual conversational workflows to analyze loan agreements. Integrate cutting-edge frontier Large Language Models (LLMs) such as GPT-4o, Claude 3.5 Sonnet, and Llama 3 with advanced orchestration frameworks (LangChain, LlamaIndex, and AutoRAG), producing high-fidelity semantic search engines, recursive document summarizations, and complex Retrieval-Augmented Generation (RAG) platforms. Develop highly reliable, multi-variate time-series forecasting models and complex anomaly detection engines utilizing deep LSTM, traditional ARIMA, and Meta Prophet architectures to radically optimize corporate predictive planning and detect complex financial fraudulent transaction signatures. Build, provision, and maintain resilient, multi-region cloud infrastructure natively on AWS utilizing S3, EC2 instances, SageMaker endpoints, Lambda serverless computing, and AWS Glue ETL catalogs, fully automated via declarative continuous integration and continuous deployment (CI/CD) workflows driven by Docker, Kubernetes, Jenkins, and GitHub Actions. Incorporate absolute transparency and governance standards utilizing SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methodologies, enabling risk compliance officers to easily interpret, trace, and audit complex black-box predictive outputs. Designed and deployed high-performance real-time model inference pipelines utilizing Python, FastAPI, and Redis pub/sub channels to stream sub-second model telemetry, financial predictions, and risk scoring outputs to downstream consuming services and executive reporting systems. Enforce rigorous engineering practices by structuring comprehensive automated test coverage with Pytest, UnitTest, and Selenium, driving code quality metrics past 95% via SonarQube gate keeping, while authoring declarative Infrastructure as Code blueprints using Terraform. Lead weekly technical agile engineering sprints, providing technical direction, architectural review, and code quality mentorship to a cross-functional team of 12 full-stack engineers and data scientists. Modernize historical legacy Java applications into clean, modular Python microservices, drastically cutting infrastructure computational costs by 35% and minimizing memory footprints across high-throughput data processing nodes. AI/ML Engineer | Elevance Health Indianapolis, IN December 2022 January 2025 Engineered robust, highly asynchronous enterprise-grade backend microservices using Python, FastAPI, and AsyncIO to efficiently handle massive parallel workloads, cutting target system latency down by a significant 42% across claims processing pipelines. Deployed scalable analytics engines and machine learning pipelines natively within Azure Cloud utilizing Azure ML studios, Synapse Analytics, Data Factory, Cognitive Services, and App Service environments to process HIPAA-regulated health data records. Designed, scaled, and tuned high-volume ETL data ingestion pipelines leveraging Apache Spark clusters, Hadoop big data ecosystem, Apache Airflow scheduling, and MongoDB, accelerating structural preprocessing times of unstructured diagnostic records. Conducted high-precision, hyperparameter optimization experiments utilizing Optuna, GridSearchCV, and RandomSearchCV frameworks to maximize accuracy, precision, and F1-scores across complex patient readmission regression and health-risk classification algorithms. Constructed state-of-the-art computer vision solutions using deep Convolutional Neural Networks (CNNs) alongside sequential processing engines using LSTMs to process intricate operational healthcare records, scanned charts, and longitudinal patient health histories. Centralized enterprise architectural telemetry, structured auditing, and model drift identification workflows by implementing the ELK Stack (Elasticsearch, Logstash, Kibana) alongside highly polished corporate Power BI interactive visualization dashboards. Developed real-time AI-assisted clinical decision support APIs leveraging FastAPI, enabling medical professionals to consume disease diagnosis predictions, risk scores, and treatment recommendations through secure, high-throughput RESTful endpoints. Maintained absolute security compliance with healthcare industry frameworks, embedding strict RBAC (Role-Based Access Control), end-to-end data encryption mechanisms at rest and in transit, and thorough OWASP top-10 security scanning into deployment lifecycles. Created automated end-to-end user-acceptance testing infrastructure utilizing Pytest-bdd and Cypress, ensuring ultra-reliable web-app performance while minimizing continuous delivery failure rates in fast-paced Agile environments. Optimized data query logic within relational and NoSQL frameworks by executing database index cleanups, deep query analysis, and object-relational mapping (SQLAlchemy, Django ORM) refinements to manage high-volume transaction databases. Machine Learning Engineer | Southwest Airlines Dallas, TX February 2021 November 2022 Developed and scaled modular backend services utilizing Flask and core Python to compute actionable flight route and fueling optimization algorithms, integrating predictive ML model outputs into operational decision systems through clean RESTful API contracts. Designed highly structured, optimized MySQL relational database schemas and engineered automated feature extraction pipelines utilizing SQLAlchemy ORM to feed live operational flight data into predictive flight delay models. Utilized RabbitMQ as a core distributed message broker to seamlessly handle asynchronous background worker execution, cron-job scheduling, and real-time streaming data ingestion feeds from weather and radar network endpoints. Packaged complex Python application structures into self-contained Docker containers and successfully deployed containerized workloads across cloud ecosystems including Heroku, Azure App Services, and AWS ECS configurations. Established robust automated software continuous integration configurations using Pytest, orchestrating automated build-and-test deployment pipelines via Azure DevOps to actively suppress pipeline regression errors. Collaborated intimately with flight operations managers, UX research teams, and external aviation data partners to collect technical requirements and transform ambiguous logic into highly scalable production code codebases. Refactored legacy code blocks into modern, performance-oriented Python constructs, effectively incorporating multi-threading and multi-processing modules to accelerate long-running analytical report compilation tasks. Leveraged Python scientific computing libraries including NumPy, Pandas, and SciPy to perform granular historical flight data crunching, parsing millions of transactional rows daily to output fuel-burn KPIs. Maintained meticulous documentation including Swagger/OpenAPI specifications for internal developers, enabling rapid onboarding and unified team adherence to corporate API standards. Python Developer | LTIMindtree Mumbai, India December 2016 July 2019 Authored highly efficient, modular backend architectures utilizing Flask and Python, processing complex structured multi-source enterprise operations via Pandas and NumPy to compile executive financial and analytical performance reports. Configured, tuned, and optimized relational data schemas inside enterprise MySQL database systems, writing complex, high-performance SQL queries and leveraging SQLAlchemy ORM to build smooth backend data-model connectivity. Maintained strict version control configurations and collaborative peer-to-peer code review processes leveraging Git and GitHub within high-speed, fast-paced Agile/Scrum development sprints. Developed elegant and reusable frontend views and UI building blocks leveraging React, JavaScript, and CSS3, significantly enhancing client-side interactivity, navigation speeds, and overall visual aesthetics. Designed and executed comprehensive backend unit testing paradigms with the Python UnitTest framework, systematically tracking down code regressions and identifying software boundary vulnerabilities early in the QA cycle. Integrated secure third-party payment gateways, enterprise billing APIs, and corporate reporting utilities into customer-facing web platforms with 100% uptime reliability. Collaborated with dev teams to configure automated build scripts using Jenkins, migrating manual deployment activities into modern automated development pipelines. Utilized Redis caching layers to store frequently requested, slow-moving analytical queries, reducing target database read traffic loads by over 50% during peak operations. Software Developer | Ahex Technologies Hyderabad, India July 2014 November 2016 Programmed feature-rich, high-performance web applications using the Python Django framework, strictly maintaining robust Object-Oriented Programming (OOP) paradigms to build highly flexible, secure, and easily maintainable clean code bases. Designed, documented, and exposed secure RESTful web services integrated with JSON Web Tokens (JWT) and secure middleware filters for robust corporate user authentication and encrypted data exchange operations. Optimized historical relational PostgreSQL database instances by creating specialized database indices, modernizing data schemas, and tuning heavy JOIN operations to seamlessly support high-volume transaction loads. Constructed interactive, pixel-perfect frontend layouts using HTML5, CSS3, JavaScript, and Bootstrap, creating cross-browser compatible, responsive user interfaces across mobile and desktop browsers. Utilized Git for routine code branches, merge conflicts resolution, and software version history preservation, collaborating with lead engineers to write technical system manuals. Integrated custom Celery task queues backed by Redis to offload heavy email notifications, report generations, and system clean-ups into separate background processes. Fixed critical application bugs, handled security hotfixes, and conducted standard code maintenance on active client production environments, maximizing system availability benchmarks. TECHNICAL INDUSTRY CERTIFICATIONS AWS Certified Solutions Architect Associate (Amazon Web Services) Microsoft Certified: Azure AI Engineer Associate (Microsoft) Fundamentals of Deep Learning Certification (NVIDIA Deep Learning Institute) ACADEMIC EDUCATION Master of Science in Computer Science (or Related Technical Discipline) University of Missouri-Kansas City (UMKC) Kansas City, MO August 2019 - December 2020 Bachelor of Technology in Computer Science & Engineering (or Related Technical Discipline) Kakatiya Institute of Technology and Science (KITSW) Warangal, India August 2010 - May 2014 Keywords: continuous integration continuous deployment quality analyst artificial intelligence machine learning user interface user experience javascript business intelligence sthree Missouri Texas Virginia