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Sree vishnu raju Dommaraju - Gen AI/ML Engineer

[email protected]

Location: Dallas, Texas, USA

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Sree vishnu raju Dommaraju Senior GenAI Engineer Email:[email protected] Mobile: +1 (971)727-7745 LinkedIn: https://www.linkedin.com/in/sreevishnurajud PROFESSIONAL SUMMARY Senior AI/ML and Generative AI Engineer with 10+ years of experience delivering enterprise-scale solutions across Machine Learning, Data Engineering, Backend Development, and Cloud-Native Architectures supporting multiple industry domains. Extensive experience designing enterprise Generative AI platforms using Vertex AI, GPT-4 Class LLMs, Claude Models, LangChain, LangGraph, and Retrieval-Augmented Generation (RAG) frameworks supporting conversational intelligence. Strong expertise building scalable distributed data pipelines using Python, PySpark, Apache Spark, SQL, Pandas, and NumPy transforming enterprise datasets into analytics-ready and AI-ready architectures supporting large-scale operations. Hands-on experience implementing semantic retrieval systems using FAISS, Pinecone, embedding pipelines, semantic reranking, and vector similarity search enabling contextual enterprise knowledge discovery and intelligent document retrieval capabilities. Proficient developing advanced Machine Learning and NLP solutions using TensorFlow, Scikit-learn, Spark MLlib, MLflow, and modern AI orchestration frameworks supporting enterprise automation and predictive analytics initiatives successfully. Experienced building enterprise chatbots and multi-agent AI systems using LangChain, LangGraph, Prompt Engineering, Tool Calling, LoRA, QLoRA, and PEFT fine-tuning techniques improving contextual reasoning capabilities significantly. Skilled designing cloud-native AI platforms across Google Cloud Platform, AWS, and Microsoft Azure using Vertex AI, Azure Databricks, Azure Machine Learning, and AWS SageMaker supporting distributed enterprise infrastructures. Strong experience developing scalable backend services and inference APIs using Python, FastAPI, REST APIs, Microservices, Apache Kafka, and distributed event-driven architectures supporting enterprise operational workflows efficiently. Experienced implementing enterprise MLOps and GenAIOps ecosystems using Docker, Kubernetes, GKE, AWS EKS, Terraform, GitHub Actions, Jenkins, and Azure DevOps supporting scalable automated AI deployment pipelines successfully. Strong expertise managing enterprise datasets using PostgreSQL, MongoDB, Redis, Cloud Storage, Amazon S3, and Azure Data Lake Storage Gen2 supporting secure and highly scalable enterprise analytics environments consistently globally efficiently. Proven ability implementing enterprise monitoring and observability solutions using Prometheus, Grafana, Cloud Monitoring, AWS CloudWatch, Azure Monitor, and RBAC ensuring platform reliability and operational transparency across environments. Experienced collaborating within Agile Scrum environments participating in sprint planning, backlog refinement, architecture reviews, stakeholder discussions, and enterprise solution delivery across cross-functional engineering teams successfully. TECHNICAL SKILLS Programming & Data Processing Python (NumPy, Pandas), Java, JavaScript, SQL, PySpark, Apache Spark Generative AI & Agentic Systems GPT-4 Class LLMs, Claude Models, AWS Bedrock, Vertex AI, Prompt Engineering, Chain-of-Thought Prompting, Few-Shot Prompting, Embeddings, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Conversational AI, Semantic Search, Tool Calling, Function Calling, Multi-Agent AI Systems, LoRA, QLoRA, PEFT, Prompt Versioning, Retrieval Tuning, Semantic Reranking Machine Learning & NLP TensorFlow, Scikit-learn, Spark MLlib, MLflow, Predictive Analytics, Fraud Detection, Classification Models, Customer Analytics, Feature Engineering, Hyperparameter Tuning, Forecasting, Model Evaluation, Inference Optimization MLOps & Deployment Docker, Kubernetes, AWS EKS, GKE, Terraform, Jenkins, GitHub Actions, Azure DevOps, CI/CD, MLflow, Evidently AI, Containerization, Model Deployment, Batch Inference Pipelines, Autoscaling, Rolling Deployments Backend & API Development FastAPI, Spring Boot, REST APIs, Microservices, API Integration, Backend Development, Object-Oriented Programming (OOP), Modular Architecture, Event-Driven Architecture Data Engineering & Distributed Systems Apache Spark, Apache Kafka, ETL Pipelines, Distributed Processing, Data Pipelines, Batch Processing, Real-Time Streaming, AWS Glue, Cloud Dataflow, Azure Data Factory, Delta Lake, Parquet, Avro, JSON, Data Transformation, Data Ingestion Databases & Storage PostgreSQL, MySQL, MongoDB, Redis, Amazon S3, Google Cloud Storage (GCS), Azure Data Lake Storage Gen2, Amazon Redshift, Pinecone, FAISS Cloud & AI Platforms AWS (AWS SageMaker, AWS Bedrock, AWS Lambda, AWS EKS, Amazon S3, Amazon Redshift, AWS CloudWatch), Google Cloud Platform (Vertex AI, Cloud Functions, Cloud Storage, GKE, Cloud Monitoring, Cloud Dataflow), Microsoft Azure (Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Machine Learning, Azure Monitor) Visualization & Monitoring Prometheus, Grafana, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, MLflow, Evidently AI, Observability, Model Monitoring Security & Governance AWS IAM, Azure Active Directory, RBAC, Encryption, Secure Access Control, Regulatory Compliance, AI Governance Frontend Technologies HTML5, CSS3, Bootstrap Methodologies & Practices Agile Scrum, SDLC, Technical Documentation, Enterprise Application Development, Scalable Architectures, Distributed Systems, Cloud-Native Applications Operating Systems & Platforms Linux PROFESSIONAL EXPERIENCE Client: Quest Health, Plano, Texas. Aug 2023 Present. Role: Gen AI Engineer. Spearheaded enterprise Generative AI healthcare platform development using AWS SageMaker, Bedrock, GPT-4 Class LLMs, LangChain, and RAG pipelines improving contextual clinical support response accuracy by 38% across operational healthcare systems. Architected scalable Microservices-based AI ecosystem using AWS Lambda, Amazon API Gateway, Apache Kafka, FastAPI, and event-driven distributed architectures supporting enterprise healthcare automation and intelligent conversational workflows. Collaborated within Agile Scrum environments participating in sprint planning, backlog refinement, architecture discussions, sprint reviews, and stakeholder demonstrations while coordinating closely with product owners and enterprise healthcare engineering. Built enterprise healthcare ingestion pipelines using Python, Apache Kafka, AWS Lambda, REST APIs, and Amazon S3 processing laboratory records, healthcare datasets, and enterprise clinical documentation across distributed healthcare platforms. Developed distributed data processing workflows using PySpark, Apache Spark, Pandas, and AWS Glue performing semantic chunking, preprocessing, metadata extraction, and embedding preparation for large-scale healthcare knowledge repositories. Managed enterprise healthcare datasets using Amazon S3, PostgreSQL, MongoDB, Redis, and Amazon Redshift supporting scalable storage, intelligent caching, operational querying, and enterprise AI retrieval optimization across healthcare environments. Implemented semantic retrieval infrastructure using Pinecone, FAISS, vector embeddings, and semantic similarity search pipelines enabling contextual enterprise knowledge discovery and intelligent healthcare document retrieval capabilities. Designed intelligent healthcare conversational assistants using AWS Bedrock, Claude Models, GPT-4 Class LLMs, LangChain, and LangGraph supporting multi-turn contextual interactions across enterprise healthcare operational systems. Integrated enterprise repositories with Retrieval-Augmented Generation (RAG) pipelines using Pinecone vector databases, embedding orchestration, and semantic reranking frameworks improving grounding quality and reducing hallucinations in healthcare applications. Applied advanced fine-tuning strategies using LoRA, QLoRA, and PEFT customizing enterprise large language models for healthcare terminology understanding, operational reasoning, and intelligent workflow automation requirements successfully. Optimized enterprise AI response quality through Prompt Engineering, retrieval tuning, semantic reranking, prompt versioning, and inference optimization improving contextual relevance and reducing inaccurate healthcare recommendations. Leveraged enterprise AI orchestration frameworks using LangChain, LangGraph, Tool Calling, and Function Calling enabling multi-agent reasoning, workflow execution, and intelligent healthcare automation capabilities across distributed enterprise systems. Followed modular architecture and Object-Oriented Python programming principles implementing reusable enterprise components, reducing code complexity, improving maintainability, and supporting scalable GenAI application development. Conducted enterprise experimentation and evaluation workflows using Agentic Workflow, MLflow, Evidently AI, offline validation frameworks, and A/B testing techniques comparing prompts, embeddings, reranking models, and retrieval strategies improving semantic relevance. Experienced implementing GitOps workflows and release engineering practices using GitHub Actions, Jenkins, Terraform, Docker, Kubernetes, AWS EKS, and GKE automating CI/CD pipelines, infrastructure provisioning, application deployments, rollback strategies, configuration management, and enterprise release orchestration across cloud-native environments. Containerized enterprise AI inference applications using Docker and Amazon ECR ensuring consistent runtime environments across development, QA, staging, and enterprise production deployment environments supporting scalable healthcare operations. Managed scalable AI infrastructure using Kubernetes on AWS EKS, autoscaling policies, GPU-enabled inference workloads, and distributed orchestration services supporting high-availability enterprise conversational AI healthcare platforms reliably. Automated CI/CD workflows using GitHub Actions, Jenkins, and Terraform enabling infrastructure provisioning, model deployment, configuration management, and enterprise application release processes across cloud-native healthcare platforms. Provisioned and managed AWS cloud infrastructure using Terraform, AWS IAM, Amazon CloudWatch, Amazon S3, AWS Lambda, and AWS networking services ensuring secure, scalable, and compliant enterprise AI platform deployments. Configured enterprise monitoring and observability solutions using Prometheus, Grafana, AWS CloudWatch, and Evidently AI tracking inference latency, token utilization, throughput metrics, model drift, and infrastructure health across healthcare AI. Developed technical documentation, API specifications, architecture diagrams, operational runbooks, Swagger documentation, and knowledge transfer materials supporting enterprise-wide adoption of scalable Generative AI engineering best practices. Environment: Python, PySpark, Apache Spark, AWS SageMaker, AWS Bedrock, GPT-4 Class LLMs, LangChain, LangGraph, RAG, Prompt Engineering, Pinecone, FAISS, Apache Kafka, FastAPI, REST APIs, GitOps workflows, release engineering, AWS Lambda, Amazon S3, PostgreSQL, MongoDB, MLflow, Docker, Kubernetes (AWS EKS), Terraform, GitHub Actions, Jenkins, Prometheus, Grafana, AWS CloudWatch, CI/CD, Agile Scrum, Linux. Scrum. Client: Regions Bank, Dallas, Texas. April 2021 July 2023 Role: AI ML Engineer. Developed enterprise machine learning solutions using Python, TensorFlow, and Vertex AI enabling Regions Bank teams to improve fraud detection and customer analytics operations across large-scale banking environments. Architected cloud-native machine learning platforms using Microservices, REST APIs, Apache Kafka, and GCP services supporting scalable banking analytics and prediction workflows across distributed enterprise environments. Created distributed data processing workflows using PySpark and Apache Spark performing cleansing, aggregation, transformation, and feature engineering supporting predictive analytics systems across banking infrastructures. Built scalable ingestion pipelines using Apache Kafka, REST APIs, GCP Dataflow, and Python to collect transactional datasets, customer records, and enterprise banking information across highly distributed mission-critical operational datasets. Collaborated within Agile Scrum teams participating in sprint planning, backlog refinement, and stakeholder discussions coordinating closely with engineering teams and product owners across large enterprise banking initiatives. Managed enterprise banking datasets using PostgreSQL, MongoDB, Redis, and GCP supporting scalable storage, caching, querying, and advanced enterprise analytics optimization capabilities across secure enterprise environments. Designed feature engineering and predictive modeling workflows supporting fraud detection, customer segmentation, risk analysis, and intelligent banking analytics applications across large enterprise operational banking systems successfully Implemented machine learning workflows using TensorFlow, Scikit-learn, and Vertex AI supporting fraud prediction, customer behavior analytics, and classification model development across enterprise banking applications. Optimized machine learning workflows using hyperparameter tuning, feature selection, model evaluation, and inference optimization improving prediction accuracy and processing efficiency across banking analytics platforms. Utilized object-oriented Python programming principles and modular architectures reducing code complexity, improving maintainability, and supporting scalable machine learning application development across banking solutions. Conducted model evaluation experiments using MLflow and statistical validation techniques comparing algorithms, training configurations, and feature engineering approaches improving reliability across banking analytical systems. Experienced implementing GitOps workflows and release engineering practices using GitHub Actions, Jenkins, Terraform, Docker, Kubernetes, AWS EKS, and GKE automating CI/CD pipelines, infrastructure provisioning, application deployments, rollback strategies, configuration management, and enterprise release orchestration across cloud-native environments. Containerized machine learning microservices using Docker and Google Artifact Registry ensuring consistent runtime environments across development, staging, testing, and enterprise production deployments across banking applications. Deployed scalable inference workloads using Kubernetes on GKE configuring autoscaling policies, rolling deployments, and enterprise machine learning infrastructure services across highly distributed global enterprise banking environments. Automated CI/CD pipelines using GitHub Actions, Jenkins, and Terraform supporting infrastructure provisioning, model deployment, configuration management, and scalable deployment automation workflows across large enterprise environments. Implemented monitoring dashboards using Prometheus, Grafana, and GCP Monitoring tracking model latency, infrastructure performance, API throughput, and operational machine learning metrics across enterprise systems. Environment: Python, PySpark, Apache Spark, SQL, TensorFlow, Scikit-learn, Vertex AI, GCP Dataflow, Apache Kafka, REST APIs, Microservices, PostgreSQL, MongoDB, Redis, MLflow, Docker, Google Artifact Registry, Kubernetes (GKE), Terraform, GitHub Actions, Jenkins, Prometheus, Grafana, GCP Monitoring, Feature Engineering, Predictive Modeling, Hyperparameter Tuning, Inference Optimization, Fraud Detection, CI/CD, Agile Scrum, Linux. Client: State of New York, New York. May 2018 March 2021 Role:Senior Data Engineer. Designed enterprise data ingestion pipelines using Azure Data Factory, Python, and SQL processing public service datasets while ensuring secure governance and regulatory compliance consistently across multiple state departments. Built distributed preprocessing workflows using PySpark on Azure Databricks cleaning citizen service datasets and transforming operational records into machine learning ready analytical features efficiently for large-scale analytics initiatives. Engineered scalable data transformation pipelines using Apache Spark aggregating transportation, taxation, and public administration datasets supporting predictive analytics and operational forecasting initiatives consistently across agencies. Integrated operational datasets from SQL databases, REST APIs, Apache Kafka, and Python enabling continuous ingestion supporting enterprise analytics and public sector reporting systems effectively across statewide operational environments. Developed feature engineering workflows using Pandas, PySpark, and Python generating citizen engagement metrics, service utilization indicators, and operational forecasting attributes successfully across diverse government service platforms. Stored curated analytical datasets within Azure Data Lake Storage Gen2 using Delta Lake versioning to ensure reproducible analytical workflows and secure enterprise data storage consistently across distributed analytics environments. Implemented secure access control mechanisms using Azure Active Directory and RBAC policies restricting analytics access to sensitive government operational datasets consistently across departments and enterprise administrative platforms successfully. Designed optimized analytical schemas within Azure Synapse Analytics enabling analysts retrieving predictive modeling datasets efficiently while maintaining governance across public administration systems successfully for enterprise reporting requirements. Built predictive analytics models using Scikit-learn and Python analyzing operational service records supporting resource planning, citizen engagement forecasting, and administrative optimization initiatives successfully across statewide departments. Implemented classification models using Spark MLlib identifying operational bottlenecks and predicting public service demand fluctuations improving state resource planning and administrative efficiency consistently across large-scale government programs. Conducted exploratory data analysis using NumPy, Pandas, and Python identifying service utilization patterns and operational performance trends across government datasets successfully supporting strategic administrative decision-making daily. Containerized analytical workflows using Docker ensuring consistent runtime environments for model training pipelines and batch analytics workloads across enterprise infrastructure successfully supporting scalable cloud-native deployment processes daily. Deployed batch inference pipelines using Azure Machine Learning generating operational forecasts consumed by government departments supporting administrative planning and resource allocation decisions consistently across multiple statewide public service initiatives. Automated CI/CD pipelines using Azure DevOps enabling controlled deployment of analytical workflows supporting predictive analytics initiatives and distributed data processing systems consistently across enterprise cloud infrastructure environment. Implemented monitoring dashboards using Azure Monitor tracking pipeline performance, prediction workloads, and operational health of enterprise analytics infrastructure supporting public sector operations consistently across mission-critical government. Environment: Python, PySpark, Apache Spark, SQL, Pandas, NumPy, Scikit-learn, Spark MLlib, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Machine Learning, Azure Data Lake Storage Gen2, Delta Lake, Azure Active Directory, Azure Monitor, Azure DevOps, Apache Kafka, REST APIs, Docker, RBAC, CI/CD, Predictive Analytics, Feature Engineering, Linux. Client: Publix, Lakeland, Florida. Nov 2016 April 2018 Role: Python Backend Developer. Engineered scalable backend applications using Python, SQL, and REST APIs supporting retail supply chain operations, inventory tracking, enterprise order management, and distributed business workflows across large-scale retail environments. Developed high-performance backend services using Python and modular architectures enabling seamless integration between operational systems, reporting platforms, enterprise analytical applications, and real-time retail processing pipelines. Crafted distributed data processing workflows using PySpark and Apache Spark handling large-scale retail datasets supporting operational reporting, near real-time analytics pipelines, and enterprise inventory forecasting requirements across business. Integrated enterprise applications with relational databases, flat files, Apache Kafka, and cloud-based services ensuring reliable backend communication, scalable integrations, and fault-tolerant enterprise data exchange mechanisms across systems. Optimized backend database operations using advanced SQL queries, indexing strategies, caching techniques, and performance tuning improving application responsiveness, transaction efficiency, and enterprise reporting performance across distributed operational platforms. Implemented secure API-driven backend frameworks using Python supporting authentication, operational data access, transaction processing, scalable enterprise communication layers, and secure integrations across retail operational business. Collaborated with frontend developers, analytics teams, and business stakeholders translating operational requirements into scalable backend solutions supporting enterprise retail applications, reporting systems, and distributed operational business. Automated backend deployment workflows using Docker, Kubernetes, Git, and Jenkins enabling streamlined application delivery, infrastructure consistency, automated releases, and reliable runtime environments across distributed enterprise. Strengthened application security using AWS IAM, encryption standards, and Role-Based Access Controls (RBAC)protecting enterprise operational services, backend processing environments, sensitive retail information, and distributed application infrastructure resources. Maintained reusable backend components, technical documentation, and modular service libraries improving maintainability, reducing development effort, accelerating enterprise feature delivery timelines, and supporting scalable backend engineering. Environment: Python, PySpark, Apache Spark, SQL, Apache Kafka, AWS Lambda, Amazon S3, Parquet, Avro, JSON, Docker, Kubernetes, Jenkins, Git, AWS IAM, Linux, Data Pipelines, Distributed Processing, CI/CD, Role-Based Access Control (RBAC), Encryption, Retail Analytics. Client: Apollo, Mumbai, India. July 2014- Sep 2016 Role: Full Stack Developer. Developed healthcare web applications using Java, Spring Boot, Python, JavaScript, and SQL supporting patient management and hospital operations workflows consistently across enterprise healthcare environments supporting mission-critical clinical. Architected multi-tier healthcare platforms using REST APIs, Microservices, HTML, CSS, and JavaScript enabling scalable patient record management across hospital systems supporting high-volume transactional healthcare processing. Collaborated within Agile Scrum teams participating in sprint planning, backlog refinement, and technical discussions coordinating closely with developers, testers, and healthcare stakeholders ensuring seamless cross-functional collaboration regularly. Built backend data integration workflows using Python, SQL, and REST APIs collecting patient records, operational datasets, and hospital reporting information efficiently across environments supporting enterprise healthcare analytics. Developed responsive frontend interfaces using HTML5, CSS3, Bootstrap, and JavaScript improving usability, accessibility, and operational efficiency across enterprise healthcare applications supporting enhanced patient engagement. Managed healthcare application databases using MySQL, PostgreSQL, and SQL supporting scalable storage, transactional processing, query optimization, and reporting requirements consistently across environments ensuring reliable enterprise database. Implemented server-side business logic using Java, Spring Boot, and Python supporting patient workflows, appointment scheduling, billing systems, and healthcare operational processes efficiently supporting large-scale hospital administration. Optimized complex SQL queries, indexing strategies, and backend processing workflows improving application response times and ensuring reliable healthcare transaction processing consistently across environments supporting critical business continuity. Containerized enterprise healthcare applications using Docker and deployed services on Linux environments ensuring consistent runtime configurations across development and production systems supporting scalable cloud-ready infrastructure successfully. Architected multi-tier healthcare platforms using REST APIs, Microservices, AWS EC2, HTML, CSS, and JavaScript enabling scalable patient record management and cloud-enabled healthcare transaction processing across enterprise hospital systems. Containerized enterprise healthcare applications using Docker and deployed services on Linux and AWS cloud environments ensuring scalable runtime configurations across development and production healthcare systems successfully. Environment: Java, Spring Boot, Python, JavaScript, HTML5, CSS3, Bootstrap, SQL, AWS EC2, AWS cloud environments, REST APIs Microservices, MySQL, PostgreSQL, Docker, Linux, Agile Scrum, Backend Development, Frontend Development, Database Management, API Integration, Healthcare Applications Education: Bachelor Of Science at Lovely Professional University. June 2014 Keywords: continuous integration continuous deployment quality analyst artificial intelligence machine learning sthree