Results-oriented Senior Full Stack Developer with 10 years of experience designing and delivering secure, scalable, cloud-native applications using C#, ASP.NET Core, Angular, React, and modern AI/ML frameworks. Skilled in architecting data-driven solutions, building CI/CD pipelines, and optimizing performance across Azure and AWS environments. Strong track record of integrating AI models with enterprise applications to drive automation and cost efficiency while maintaining regulatory compliance. Innovative Machine Learning Research Engineer and Senior Full Stack Developer with 10+ years of experience designing and deploying AI-driven enterprise solutions across financial, healthcare, and technology domains. Hands-on expertise in developing and fine-tuning large language models (LLMs), transformer architectures, and AI microservices using PyTorch, TensorFlow, LangChain, and Hugging Face. Proven success implementing Reinforcement Learning from Human Feedback (RLHF), Self-Fine Tuning (SFT), and retrieval-augmented generation (RAG) pipelines to enhance accuracy and contextual understanding. Experienced in cloud-native deployment (Azure / AWS), vector databases (Pinecone, FAISS, Neo4j, Neptune), and distributed data workflows with Databricks and Apache Spark. Familiarity with enterprise financial systems including SAP Finance, Treasury, and Payout process flows. Passionate about advancing applied AI research through model optimization, human-feedback loops, and scalable MLOps frameworks. Enterprise Architect with 12+ years of experience designing enterprise-grade full stack and AI/ML platforms across financial, healthcare, and technology domains. Expert in defining enterprise architecture standards, reference architectures, and governance models for cloud-native and AI-enabled systems.
Hi, I’m
PRITI THAKKAR
Senior AI/ML Architect and senior full stack developer
Monroe Township,NJ
Summary
Skills
- C#
- ASPNET Core 8
- Entity Framework Core
- VBNET
- WCF
- WPF
- Python (FastAPI)
- JavaScript
- TypeScript
- Angular 16
- React 18
- Redux
- NgRx
- SignalR
- Single-SPA
- LangChain
- Hugging Face
- TensorFlow
- MLflow
- AWS Bedrock
- Azure AI Services
- Databricks
- OpenAI APIs
- SQL Server
- Azure SQL
- PostgreSQL
- MongoDB
- ElasticSearch
- Microsoft Azure
- AWS (ECS, S3, Lambda)
- Docker
- Kubernetes
- Terraform
- GitHub Actions
- Azure DevOps
- Jenkins
- SonarQube
- XUnit
- Moq
- Jasmine
- Karma
- Cypress
- React Testing Library
- Application Insights
- Grafana
- Serilog
- OAuth2
- JWT
- MTLS
- Key Vault
- RBAC
- HIPAA
- SOC2
- Power BI
- SSIS
- SSRS
- Crystal Reports
- Agile/Scrum Methodologies
- Azure AD
- MS Identity Platform
- MS Graph API
- Power Platform
- Microsoft 365 Integrations
- MCP Framework
Work History
Walbrydge Technologies Inc.
Senior AI/ML Architect & Full Stack Developer
05.2024 - Current
Job overview
- Designed, developed, and deployed AI-driven microservices using C# (.NET 8) and Python (FastAPI) integrated with Azure OpenAI, AWS Bedrock, and Cognitive Services.
- Built modular Angular 16+ front-end apps using NgRx for state management, SignalR for live updates, and Single-SPA for micro-frontend integration.
- Engineered end-to-end RAG (Retrieval-Augmented Generation) workflows with LangChain, enabling contextual, dynamic AI chatbot solutions.
- Conducted Reinforcement Learning from Human Feedback (RLHF) experiments and applied Self-Fine Tuning (SFT) techniques to improve response accuracy and contextual relevance of LLMs in domain-specific applications.
- Researched, designed, and evaluated transformer-based architectures and custom embeddings to enhance contextual understanding, semantic similarity, and multi-turn conversation performance.
- Developed custom embeddings and vector storage for semantic search using FAISS and Pinecone.
- Integrated knowledge graph and vector retrieval pipelines using Neo4j and AWS Neptune to enrich contextual relationships and improve retrieval-augmented reasoning accuracy.
- Designed and tested novel embedding comparison strategies using cosine similarity, vector quantization, and clustering to improve context retrieval accuracy by 25%.
- Leveraged Databricks for scalable ETL, data transformation, and model training workflows integrated with Azure Data Factory.
- Utilized Apache Spark on Databricks for distributed data preprocessing, feature extraction, and large-scale training data curation supporting ML model experiments.
- Implemented MLflow for model tracking, experiment comparison, and deployment governance.
- Designed enterprise MLOps frameworks with reproducible pipelines, controlled model promotion, rollback strategies, and compliance-aligned audit trails.
- Defined monitoring strategies for model drift, data quality, inference stability, and operational reliability across production AI systems.
- Developed, trained, and fine-tuned transformer models using PyTorch and Hugging Face Transformers, leveraging pre-trained checkpoints for domain adaptation and efficiency improvements.
- Built and optimized RESTful APIs and gRPC services for AI and data-analytics modules, improving reliability and throughput.
- Containerized distributed microservices with Docker, orchestrated deployments via Azure Kubernetes Service (AKS) and Helm.
- Architected vendor-neutral, cloud-agnostic AI platforms designed for portability across Azure, AWS, and GCP environments.
- Implemented enterprise API management patterns including security, throttling, versioning, and observability for AI and data services.
- Architected CI/CD pipelines using GitHub Actions and Terraform for infrastructure provisioning and automated testing.
- Enhanced AI model efficiency by 40% through quantization, pruning, and inference optimization.
- Contributed to applied ML research by benchmarking multiple LLM architectures (GPT-3.5, Claude, Titan) for latency, coherence, and factual consistency across diverse datasets.
- Collaborated across AI, DevOps, and Cloud Security teams to ensure compliance and governance alignment.
- Mentored junior developers on .NET microservices, CI/CD, and AI integration best practices.
- Defined enterprise reference architecture for generative AI platforms covering data ingestion, retrieval, model orchestration, deployment, and monitoring.
- Established AI/ML architecture standards including model lifecycle management, promotion workflows, versioning, and auditability.
- Led architecture governance through design reviews, technical standards enforcement, and cross-team alignment.
Bank of America
Senior Architect AI/ML Engineer
05.2022 - 04.2024
Job overview
- Led modernization of enterprise banking applications from legacy .NET Framework to .NET 8 microservices using C# and ASP.NET Core Web API.
- Integrated AI/ML analytics via Azure OpenAI Service, AWS Bedrock, and Cognitive Services to automate fraud detection and financial insight workflows.
- Built React 18+ dashboards with Hooks, Context API, and Redux for real-time financial and compliance monitoring.
- Migrated Angular 8 modules to Angular 16+ using NgRx Store and RxJS, improving performance by 30%.
- Developed and optimized RESTful APIs and gRPC services connecting payments, customer data, and compliance systems.
- Enforced clean architecture and TDD principles, increasing code coverage from 55% to 90% with xUnit and Moq.
- Automated CI/CD pipelines using Azure DevOps YAML and Terraform, enabling blue-green deployments and IaC provisioning.
- Containerized workloads with Docker and orchestrated using Azure Kubernetes Service (AKS) achieving 99.99% uptime.
- Implemented secure OAuth2 and JWT authentication with Azure Active Directory (B2C) integration.
- Developed React/Angular dashboards and .NET Core APIs for financial data processing.
- Built and maintained large-scale React 18 applications using functional components and Hooks for cleaner, modern, and maintainable UI development.
- Implemented scalable state management patterns using Redux for cross-application state and Context API for localized/shared state to keep components lightweight.
- Developed reusable and modular components to reduce duplication and maintain UI consistency across multiple product modules.
- Optimized rendering performance using memoization techniques (useMemo, useCallback) to reduce unnecessary re-renders and improve UI responsiveness.
- Integrated API data flows using Axios + React Query / fetch patterns with graceful error handling and loading states.
- Worked closely with UX/Product teams to ensure components were accessible, responsive, and aligned with enterprise UI standards.
- Integrated Azure AD B2C authentication for external partners with fine-grained access control and token validation.
- Partnered with data-science teams to productionize MLflow models for credit-risk scoring and forecasting.
- Built Databricks pipelines for high-volume ETL; reduced nightly batch runtime by 40%.
- Established logging standards using Serilog and Application Insights for proactive incident monitoring.
- Implemented scalable state management patterns using Redux for complex application state and Context API for localized shared state to maintain clean and maintainable components.
- Mentored a 10-member developer team in .NET 8, CI/CD, and AI integration practices improving sprint velocity by 25%.
- Worked with cybersecurity teams to ensure SOC2 and PCI DSS compliance for all AI-enabled solutions.
- Worked within the Microsoft enterprise ecosystem using Azure AD, MS Graph APIs, and secure identity frameworks.
- Applied MCP-aligned cloud architecture patterns supporting governance, identity, compliance, and application modernization.
- Partnered with banking and treasury teams on workflows involving payments, settlements, and payout reconciliation, supporting SAP-compatible financial processes.
- Participated in integration discussions connecting .NET microservices with SAP-aligned financial posting and transaction validation flows.
- Diagnosed and resolved API and SQL performance issues using execution plans, optimized indexing, caching strategies, and async patterns to reduce latency and improve overall throughput.
WellPoint
Senior .NET Software Developer, Full Stack
09.2020 - 04.2022
Job overview
- Designed and implemented enterprise-grade C#/.NET Core Web APIs for healthcare claims, eligibility, and provider management.
- Integrated APIs with EPIC and HL7 systems to synchronize real-time patient and insurance data.
- Developed Angular 14+ interfaces using NgRx, SignalR, and RxJS to display live claim and eligibility updates.
- Built AI-assisted claim validation workflows with Azure Cognitive Services, automating document recognition and ICD/CPT code extraction.
- Created batch automation using Autosys, SSIS, and Azure Data Factory to handle millions of records nightly.
- Used Databricks for predictive fraud-detection models and high-volume claims analytics.
- Migrated monolithic .NET apps to containerized microservices deployed on Docker and AKS.
- Implemented TDD and full CI/CD pipelines in Azure DevOps, reducing defects and deployment times.
- Tuned APIs and SQL queries improving response times by 30%.
- Applied HIPAA-compliant encryption and RBAC; enabled continuous monitoring via Azure Security Center.
- Partnered with clinicians and analysts to embed AI insights that cut manual claim review effort by 40%.
- Led migration of on-prem data to Azure SQL and Blob Storage, integrating Power BI dashboards for executives.
- Delivered modernized SPA architecture with React and .NET Core.
- Implemented secure identity federation using Azure AD and OAuth2.
- Improved Entity Framework performance by avoiding N+1 queries, managing change tracking carefully, and using AsNoTracking for read operations to reduce memory and improve speed.
- Conducted peer code reviews, knowledge sessions, and onboarding of new engineers.
- Published detailed Swagger/Open API specs ensuring consistent partner integration.
- Delivered robust, compliant healthcare software improving processing efficiency and member satisfaction.
Creehan & Company
Senior Software Engineer (.NET)
04.2018 - 08.2020
Job overview
- Designed and delivered enterprise-grade .NET Framework / .NET Core solutions for pharmaceutical data and patient-support platforms.
- Developed modular architectures using ASP.NET MVC, Web API, and C#, employing dependency injection and repository patterns for maintainability.
- Built interactive AngularJS / Angular 10+ front ends and connected them to RESTful APIs for clinical workflow automation.
- Optimized data access with Entity Framework Core; tuned SQL queries and indexes improving throughput by 45%.
- Created Power BI and SSRS dashboards delivering real-time analytics to product and compliance teams.
- Implemented OAuth2, JWT, and RBAC to maintain HIPAA-compliant security across all applications.
- Automated build / release pipelines using Jenkins and Azure DevOps, achieving consistent deployments across all environments.
- Adopted Docker for environment parity, simplifying QA and production rollouts.
- Partnered with QA and product teams to define acceptance criteria and accelerate test coverage.
- Added full telemetry with Serilog and Application Insights to track performance and exceptions.
- Practiced Agile Scrum with sprint planning, estimation, and retrospectives for continuous improvement.
- Authored CI/CD YAML pipelines integrating automated testing and SonarQube code quality gates.
- Built ETL integrations through Azure Data Factory and Databricks to feed analytics warehouses.
- Maintained 99.9% uptime by proactive monitoring and responsive on-call support.
- Documented APIs, data flows, and deployment runbooks ensuring transparent handoffs.
Axxess
Lead .NET Developer
02.2015 - 03.2018
Job overview
- Designed and supported home health and hospice management systems using C#, ASP.NET MVC, and WCF Services.
- Built Web APIs and SOAP endpoints for scheduling, billing, and clinical documentation workflows.
- Developed AngularJS components and implemented SignalR for real-time updates on patient tasks and alerts.
- Tuned SQL Server stored procedures, triggers, and indexes, reducing average response time by 40%.
- Created SSRS and Crystal Reports for clinical and operational performance tracking.
- Implemented SSL/TLS, OAuth2, and token-based authentication to maintain HIPAA compliance.
- Migrated legacy VB.NET modules to C# / ASP.NET MVC, improving scalability and code reuse.
- Automated testing and regression suites with Selenium improving release reliability.
- Integrated with external EHR systems using REST APIs and custom ETL pipelines.
- Developed reusable libraries for auditing, validation, and centralized logging.
- Automated builds and deployments with MSBuild and PowerShell, cutting manual release time by half.
- Provided production support for multi-tenant SaaS systems hosted on Azure App Services.
- Translated business requirements into scalable, maintainable technical solutions.
- Maintained data security and PHI protection across environments.
- Mentored junior engineers in .NET coding standards and secure SDLC practices.
Education
Nagpur UniversityIndia
Master’s in Commerce
01.2009
University Overview
Key Projects Summary
Key Projects Summary
- AI Chat Automation Platform, Walbrydge Technologies, Designed and implemented a multi-agent conversational AI system using LangChain, Azure OpenAI, and AWS Bedrock that automated document analysis and real-time user support, reducing manual query handling by 65%.
- Enterprise Microservices Modernization, Bank of America, Migrated legacy banking applications to .NET 8 microservices on AKS and Docker, cutting technical debt and improving system performance by 35%.
- Healthcare Claims Intelligence, WellPoint, Developed AI-assisted claim validation workflows leveraging Azure Cognitive Services and Databricks, which reduced manual review time by 40% while maintaining HIPAA compliance.
- Data Integration & Analytics Platform, Creehan & Company, Engineered ETL pipelines and analytics dashboards using Azure Data Factory, SQL Server, and Power BI, improving operational decision-making and data consistency across departments.
- Home Health Management Solution, Axxess, Built secure, modular applications in ASP.NET MVC, AngularJS, and SignalR enabling real-time coordination between clinicians and administrators, improving field efficiency and patient satisfaction.
Ai Ml Research Experiments
Ai Ml Research Experiments
- Conversational Reinforcement Learning, Built human-feedback loops to fine-tune chatbot behavior using RLHF and reward modeling.
- Vector Retrieval Optimization, Evaluated FAISS, Pinecone, and Weaviate for embedding performance, improving retrieval precision by 25%.
- Model Efficiency Studies, Tested LoRA and PEFT adapters to reduce fine-tuning cost and latency on transformer models.
- Graph Reasoning, Integrated Neo4j-based knowledge graphs into RAG pipelines for entity-relationship-driven answer generation.
Ai Ml Tools Frameworks
Ai Ml Tools Frameworks
Azure OpenAI, AWS Bedrock, Hugging Face Transformers, LangChain, PyTorch, TensorFlow, MLflow, Scikit-learn, Databricks, Apache Spark, Pinecone, Neo4j, AWS Neptune, SQL Server, MongoDB, Azure, AWS, Docker, Kubernetes, Terraform, GitHub ActionsTechnical Achievements Summary
Technical Achievements Summary
- Reduced AI inference latency by 35% through model quantization, caching, and distributed inference optimization.
- Familiar with OpenShift workflows (Kubernetes-based container orchestration and enterprise CI/CD integration).
- Improved deployment cycles by 60% using GitHub Actions and Terraform-based CI/CD pipelines.
- Achieved 99.99% service uptime via containerized AKS deployments with proactive monitoring.
- Enhanced API response times by 45% through caching, async patterns, and SQL tuning.
- Lowered cloud spend by 40% by implementing autoscaling and reserved-instance strategies.
- Standardized model governance using MLflow for consistent versioning and auditing.
- Boosted code quality with enforced TDD and SonarQube static analysis integration.
- Accelerated ETL performance by 50% through Databricks parallel processing.
- Strengthened application security with OAuth2 + JWT and centralized role-based access control.
- Maintained compliance with HIPAA, SOC 2, and GDPR requirements across all products.
- Experience with CloudBees (enterprise Jenkins), TFS build pipelines, and automated deployment practices.
Enterprise Architecture Governance
Enterprise Architecture Governance
- Defined enterprise architecture principles and standards for cloud-native and AI-enabled platforms.
- Facilitated architecture review boards and technical audits across multiple teams.
- Established reusable platform patterns accelerating delivery across product teams.
Personal Information
Personal Information
Overview
11
years of professional experience