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Sujitha C - Sr AI / ML Engineer

[email protected]

Location: Mclean, Virginia, USA

Relocation: Yes

Visa: Green Card

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Sujitha Cherukuthota +1 (757) 936-9318 [email protected] Summary: Senior AI / Machine Learning Engineer with 10+ years of experience delivering scalable AI, Machine Learning, NLP, and cloud-native analytics solutions across Healthcare, Banking, Government, and Retail enterprise business environments. Built enterprise Agentic AI and Generative AI applications using AWS Bedrock, Azure OpenAI, LangChain, LangGraph, Crew AI, MCP, and LlamaIndex supporting intelligent automation and contextual enterprise search capabilities. Developed production-ready RAG Pipelines using Pinecone, FAISS, embeddings, semantic retrieval, contextual grounding, and retrieval-aware prompting improving enterprise AI response relevance and document discovery quality significantly. Experienced building intelligent AI agents supporting planning, reasoning, execution, validation, memory handling, and human-in-the-loop workflows across healthcare and financial operational environments using modern orchestration frameworks. Strong expertise developing Machine Learning and predictive analytics solutions using Scikit-learn, XGBoost, TensorFlow, PyTorch, Keras, and Spark MLlib supporting fraud detection, forecasting, anomaly detection, and classification initiatives. Developed transformer-based NLP Solutions using Hugging Face, BERT, GPT, spaCy, and NLTK supporting summarization, entity extraction, conversational AI, intelligent routing, document processing, semantic search, classification, and Q/A workflows. Designed scalable AI Platforms using FastAPI, REST APIs, Docker, Kubernetes, Amazon EKS, and Azure AKS supporting distributed inference services, microservices architectures, enterprise AI deployment requirements, and orchestration. Built distributed Data Engineering and ETL workflows using PySpark, Databricks, SQL, Hive, Azure Data Factory, and AWS Glue supporting large-scale analytics and machine learning data preparation activities. Implemented enterprise MLOps workflows using MLflow, GitHub Actions, Jenkins, Terraform, CI/CD Pipelines, Prometheus, and CloudWatch supporting deployment automation, monitoring, governance, and operational reliability requirements. Experienced working with healthcare and banking business domains including PBM, Prior Authorization, Claims Adjudication, Member Eligibility, Fraud Analytics, and financial operational reporting supporting enterprise AI modernization initiatives. Technical Skills: Programming languages: Python, SQL, Java, PL/SQL, Shell Scripting Agentic AI & Generative AI: AWS Bedrock, Azure OpenAI, Claude, LangChain, LangGraph, Crew AI, MCP, AI Agents, Agent Orchestration, Tool Invocation, LlamaIndex, Prompt Engineering, ReAct Reasoning, HITL, RAG Pipelines, RAGAS Machine Learning & Deep Learning: Scikit-learn, XGBoost, TensorFlow, PyTorch, Keras, Spark MLlib, MLflow, Predictive Modeling, Forecasting, Classification, Regression, Clustering, Anomaly Detection, A/B Testing, Feature Engineering NLP & Semantic Retrieval: Hugging Face, BERT, spaCy, NLTK, Pinecone, FAISS, Embeddings, Semantic Search, Hybrid Retrieval, Contextual Grounding, Vector Databases, Intelligent Document Processing Data Engineering & Big Data: PySpark, Databricks, AWS Glue, Azure Data Factory, Hive, ETL Pipelines, Data Transformation, Data Validation, Statistical Analysis, Distributed Processing Cloud Platforms & Storage: AWS, Azure, Amazon S3, AWS Lambda, Redshift, DynamoDB, Azure Blob Storage, Azure Synapse Analytics, Delta Lake Frameworks & APIs: FastAPI, REST APIs, Microservices Architecture, Distributed Systems, Workflow Automation, Enterprise Integrations DevOps / MLOps: Docker, Kubernetes, Amazon EKS, Azure AKS, GitHub Actions, Jenkins, Terraform, CloudFormation, Azure DevOps, CI/CD Pipelines, Model Lifecycle Management Monitoring & Validation: Prometheus, CloudWatch, Grafana, RAGAS, PyTest, Swagger API Validation, Model Monitoring, Performance Evaluation Visualization & Reporting: Tableau, Power BI, Matplotlib, Seaborn, Grafana, Operational Reporting, Data Visualization, Executive Dashboards Experience: HCA Healthcare Senior AI / Machine Learning Engineer Richmond, VA | Feb 2024 - Present Developed healthcare AI solutions using Python and AWS Bedrock to improve payer and provider workflows by implementing intelligent applications supporting Prior Authorization, clinical documentation retrieval across healthcare environments. Developed enterprise AI applications using FastAPI and REST APIs to integrate healthcare platforms, cloud services, and automation capabilities while delivering scalable solutions aligned with client operational requirements. Implemented healthcare solution workflows by analyzing business processes, translating requirements into technical designs, and deploying production applications supporting clinical operations and administrative healthcare functions. Integrated healthcare data sources including FHIR and HL7 interfaces with claims systems, clinical records, and provider information platforms to enable secure data exchange across enterprise healthcare applications. Developed scalable data pipelines using Python and PySpark to transform healthcare datasets, validate information quality, and prepare structured data required for analytics and intelligent application workflows. Connected SQL databases, document repositories, and cloud storage platforms through API integrations to establish reliable information flows supporting healthcare application delivery and operational automation. Implemented vector search solutions using embeddings and FAISS to retrieve relevant healthcare policies, clinical documents, and operational knowledge from large enterprise information repositories. Integrated AWS Bedrock Claude models with healthcare applications using LLM APIs and prompt workflows to provide contextual responses, document analysis, and intelligent information discovery capabilities. Developed RAG applications using LangChain, embeddings, and retrieval methods to connect healthcare knowledge sources with AI solutions while improving information accuracy and reducing unsupported responses. Created AI agent workflows using LangGraph and MCP patterns to automate healthcare processes involving tool execution, information retrieval, and multi-step operational assistance. Applied Prompt Engineering techniques to customize LLM behavior, improve response consistency, and align generated information with healthcare workflows, application requirements, and operational objectives. Developed backend services using Python FastAPI and REST APIs while applying object-oriented principles, modular components, and software engineering practices for maintainable enterprise healthcare applications. Improved application performance through API optimization and retrieval tuning by analyzing response patterns, reducing latency, and enhancing reliability across deployed healthcare technology solutions. Deployed healthcare applications using Docker containers and AWS cloud services to maintain consistent environments, support scalability, and enable reliable execution across enterprise infrastructure platforms. Implemented CI/CD pipelines using GitHub Actions and Jenkins to automate application testing, deployment workflows, and controlled releases across healthcare cloud environments. Managed cloud resources using Terraform and AWS services to configure infrastructure components, maintain deployment consistency, and support scalable healthcare AI application operations. Supported production environments by troubleshooting application issues, analyzing system behavior, and improving solution reliability across deployed healthcare applications and integrated enterprise platforms. Applied Responsible AI practices including guardrails, validation methods, and hallucination mitigation techniques to improve security, trust, and reliability within healthcare AI implementations. Monitored deployed applications using CloudWatch and logging frameworks to evaluate performance, identify operational issues, and maintain availability of healthcare technology solutions. Performed testing using PyTest and Swagger while documenting APIs, workflows, and deployment procedures to support maintenance, troubleshooting, and continuous improvement activities. ________________________________________ Environment: Python, Java, SQL, FastAPI, REST APIs, Microservices, Distributed Systems, AWS Bedrock, Claude, LLM APIs, RAG, Agentic AI, LangChain, LangGraph, MCP, Prompt Engineering, Vector Databases, Pinecone, FAISS, Embeddings, FHIR, HL7, Claims Data, Prior Authorization, Clinical Documents, PySpark, Databricks, AWS Glue, Amazon S3, AWS Lambda, Amazon EKS, Kubernetes, Docker, Terraform, GitHub Actions, Jenkins, CloudWatch, PyTest, Swagger. Sallie Mae Bank AI / Machine Learning Engineer Newark, DE | May 2022 - Jan 2024 Built healthcare GenAI applications using Python, AWS Bedrock, and FastAPI to support payer and provider workflows including Prior Authorization, clinical document retrieval, and intelligent information access across healthcare operational environments. Built scalable multi-tier applications using REST APIs, Microservices, and reusable backend components while applying object-oriented programming concepts and design patterns to integrate AI capabilities with existing healthcare enterprise platforms. Implemented healthcare data workflows integrating FHIR, HL7, clinical documents, pharmacy claims, and operational datasets to prepare structured information sources for AI-powered search, analysis, and automation applications. Developed backend services using Python, FastAPI, and API integration patterns to connect healthcare applications with LLM services, enterprise systems, and distributed components requiring secure communication and reliable execution. Created enterprise RAG solutions using LangChain, embeddings, vector databases, and retrieval optimization techniques to connect healthcare knowledge sources with LLM applications while improving response accuracy and information grounding. Integrated AWS Bedrock Claude models with healthcare applications using LLM APIs, prompt engineering techniques, and retrieval workflows to generate summaries, answer operational questions, and improve access to clinical information. Developed Agentic AI workflows using LangGraph, MCP integrations, and orchestration patterns to implement tool execution, memory handling, multi-step reasoning, and automated task completion across healthcare operational processes. Applied Responsible AI practices including AI Guardrails, hallucination mitigation, prompt validation, and response evaluation methods to improve security, reliability, and compliance considerations for healthcare AI applications. Built healthcare data pipelines using PySpark, AWS Glue, and Databricks to transform clinical datasets, validate information quality, and prepare reliable inputs for AI applications and analytical workflows. Integrated enterprise healthcare systems using Python, JavaScript, and Node.js services to support API communication, workflow automation, and interoperability between AI solutions and operational applications. Deployed AI services using Docker, AWS ECR, Amazon EKS, and CI/CD pipelines to maintain scalable cloud environments, automate application releases, and support production deployment requirements. Performed application validation using PyTest, Swagger API testing, CloudWatch monitoring, and performance analysis practices to maintain reliable AI application behavior across enterprise healthcare environments. Environment: Python, AWS Bedrock, Claude, FastAPI, REST APIs, Microservices, N-tier Architecture, Node.js, JavaScript, AWS Lambda, Amazon S3, Redshift, DynamoDB, AWS Glue, PySpark, Databricks, Pinecone, FAISS, LangChain, LangGraph, MCP, LLM APIs, RAG, Vector Databases, Prompt Engineering, AI Guardrails, Responsible AI, Docker, Amazon EKS, Kubernetes, Terraform, GitHub Actions, CloudWatch, PyTest, Swagger, FHIR, HL7, Healthcare Data Integration, Clinical Documents, Prior Authorization, Claims Data. State of California Data Scientist / ML Engineer San Francisco, CA | Feb 2020 - Apr 2022 Developed intelligent financial AI solutions using Azure OpenAI, LangChain, FastAPI, and Scikit-learn supporting fraud investigation workflows, loan servicing operations, repayment assistance, and banking document validation activities. Designed scalable AI orchestration workflows using LangChain, REST APIs, and Microservices Architecture enabling contextual reasoning, workflow automation, intelligent routing, and semi-autonomous financial support operations. Built enterprise ingestion frameworks using Python and Azure Data Factory integrating loan applications, repayment histories, customer communications, transactional datasets, and financial operational reporting records from distributed banking systems. Developed high-volume transformation pipelines using PySpark, Azure Databricks, and Azure Synapse processing financial datasets supporting fraud analytics, anomaly detection, risk scoring, and machine learning feature engineering activities. Managed governed financial storage architectures using Azure Blob Storage, Delta Lake, and Azure Synapse supporting secure banking data accessibility, enterprise analytics, and scalable financial reporting capabilities. Implemented semantic retrieval workflows using Pinecone and Azure AI Search enabling intelligent financial document discovery, contextual recommendations, fraud investigation assistance, and enterprise banking knowledge retrieval activities. Fine-tuned enterprise NLP workflows using Hugging Face, BERT, spaCy, and NLTK supporting intent recognition, text classification, entity extraction, sentiment analysis, and automated banking document processing activities. Built scalable RAG Pipelines using LangChain, embeddings, and semantic retrieval improving contextual response generation, financial document grounding, and intelligent banking assistance across operational support environments. Developed predictive fraud analytics workflows using Scikit-learn, XGBoost, TensorFlow, and MLflow supporting anomaly detection, fraud classification, model evaluation, and enterprise financial risk analysis initiatives. Conducted enterprise A/B Testing and offline validation workflows comparing fraud scoring accuracy, classification consistency, contextual retrieval quality, and operational model performance across banking AI environments. Enhanced AI response behavior using Prompt Engineering and contextual grounding techniques improving fraud investigation quality, conversational consistency, and financial document response relevance across operational banking workflows. Developed intelligent orchestration services using FastAPI and LangChain supporting fraud investigation assistance, workflow execution, contextual validation, and secure LLM integrations across enterprise banking support systems. Integrated enterprise financial APIs and operational banking systems enabling secure data retrieval, workflow automation, contextual reasoning, and intelligent response generation across customer servicing environments. Leveraged GitHub Copilot during backend API development and fraud analytics integrations improving reusable service creation, engineering productivity, and scalable financial AI application delivery workflows. Utilized MLflow and TensorFlow for experiment tracking, model evaluation, model versioning, inference optimization, and AI deployment lifecycle management across banking machine learning environments. Built scalable inference services using Docker and Azure Container Registry enabling workload standardization, runtime consistency, secure deployments, and portable AI execution across enterprise financial platforms. Deployed fraud analytics workloads on Azure AKS and Kubernetes enabling autoscaling, workload resiliency, deployment isolation, and highly available AI platform orchestration across banking operational environments. Automated enterprise deployment workflows through CI/CD Pipelines using Azure DevOps, GitHub Actions, Terraform, and Jenkins improving release governance, rollback readiness, and deployment reliability across banking AI systems. Tracked operational AI performance using Grafana, Prometheus, and model monitoring workflows identifying latency issues, infrastructure alerts, model drift, and fraud detection inconsistencies across production environment. Performed Unit Testing and API validation using PyTest and Swagger specifications ensuring stable integrations, deployment reliability, secure API functionality, and enterprise compliance across financial AI applications. Environment: Python, Azure OpenAI, LangChain, Hugging Face, BERT, Pinecone, Azure AI Search, FastAPI, REST APIs, PySpark, Azure Databricks, Azure Data Factory, Azure Synapse Analytics, Azure Blob Storage, Delta Lake, TensorFlow, Scikit-learn, XGBoost, spaCy, NLTK, MLflow, Docker, Kubernetes, Azure AKS, GitHub Actions, Azure DevOps, Jenkins, Terraform, Grafana, Prometheus, PyTest, Swagger API, RAG Pipelines, Semantic Retrieval, Fraud Detection, Anomaly Detection, Predictive Analytics, CI/CD Pipelines, Microservices Architecture, Distributed Systems. Walmart Global Tech Data Engineer Bentonville, AR | Oct 2016 - Dec 2019 Developed scalable retail data solutions using Google Cloud Storage and BigQuery, supporting high-volume data ingestion, enterprise reporting, operational analytics, and business intelligence initiatives across distributed retail environments. Built end-to-end ETL workflows using GCP, Python, and BigQuery, transforming structured and unstructured retail datasets into analytics-ready formats supporting reporting, forecasting, and operational decision-making processes. Engineered cloud-native data processing workflows using Google AI Platform, Hadoop, and Hive, enabling scalable analytics, distributed processing, predictive modeling, and enterprise modernization initiatives across reporting systems. Designed enterprise data lake architectures using BigQuery and Google Cloud Storage, improving data accessibility, reporting performance, inventory analytics, and centralized operational intelligence across retail business applications. Developed interactive reporting dashboards using Data Studio and BigQuery, enabling self-service analytics, operational visibility, and business insights for reporting analysts and cross-functional retail stakeholders. Integrated enterprise systems using SOAP and WSDL services, supporting reliable communication between inventory applications, operational platforms, and partner-facing retail systems across distributed business environments. Automated enterprise data ingestion and SQL transformation workflows using Python, PL/SQL, Hive, Oracle 10g, and DB2, improving reporting consistency, query performance, and operational data reliability significantly. Collaborated within Agile Scrum environments supporting enterprise reporting modernization, metadata management, OLTP/OLAP processing, technical documentation, and scalable analytics delivery across retail operational systems. Environment: Python, Google Cloud Platform, Google Cloud Storage, BigQuery, Google AI Platform, Data Studio, SOAP, WSDL, Hadoop, Hive, Oracle 10g, DB2, OLTP, OLAP, Metadata Management, MS Excel, MS Visio, Rational Rose, PL/SQL, PHP, SQL, Agile Scrum. Citibank Python Developer Hyderabad, India | Aug 2015 - Sep 2016 Developed Financial Reporting solutions using Python and SQL supporting transaction reconciliation, compliance tracking, operational reporting, and customer account analysis for daily banking activities and finance operations. Built scalable ETL Workflows using Python, Pandas, MySQL, and Oracle transforming transactional banking records into structured reporting datasets supporting reconciliation and financial processing requirements effectively. Designed optimized SQL Queries and multi-table joins consolidating customer transactions, account histories, and operational datasets supporting reconciliation checks, reporting accuracy, and banking data analysis activities consistently. Implemented robust Data Validation routines using Pandas and Python identifying duplicate records, missing values, transactional inconsistencies, and formatting issues within high-volume financial reporting datasets efficiently. Automated recurring reporting and Batch Processing activities using Python and Shell Scripting improving operational efficiency, reducing manual reporting efforts, and supporting scheduled financial reporting execution requirements effectively. Maintained enterprise Relational Databases including MySQL and Oracle supporting query optimization, structured data storage, transaction processing, and reliable reporting accessibility for operational banking systems consistently. Developed reusable backend scripts using Python and SQL supporting operational reporting automation, transaction monitoring, reconciliation workflows, and standardized financial data processing activities within reporting applications effectively. Performed transaction-level analysis using SQL and Pandas supporting operational investigations, reconciliation validation, compliance reporting checks, and financial reporting quality assurance activities during production processing cycles consistently. Executed Unit Testing and workflow validation activities ensuring accurate transaction calculations, stable reporting logic, reliable processing behavior, and operational consistency across enterprise financial reporting systems effectively. Collaborated within Agile Scrum teams supporting reporting enhancements, operational workflow improvements, backend processing requirements, and technical documentation activities aligned with enterprise banking reporting standards consistently. Environment: Python, SQL, Pandas, MySQL, Oracle, ETL Workflows, Data Processing, Data Transformation, Data Validation, Financial Reporting, Compliance Reporting, Batch Processing, Shell Scripting, SQL Queries, Relational Databases, Operational Reporting, Automation, Agile Scrum, Git, Linux. Keywords: continuous integration continuous deployment artificial intelligence machine learning javascript business intelligence sthree microsoft mississippi procedural language bay area Arkansas California Delaware Virginia