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Jaya Govardhan - AI/ML Engineer

[email protected]

Location: New York City, New York, USA

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JAYA GOVARDHAN KOTA Senior AI/ML Engineer/Gen AI Engineer Phone: (816)264-4419 Email: [email protected] Linkedin: https://www.linkedin.com/in/jayagovardhankota/ PROFESSIONAL SUMMARY Around 10+ years of experience in designing, developing, and deploying enterprise Artificial Intelligence (AI), Generative AI, Machine Learning, Natural Language Processing (NLP), and Data Science solutions across Banking, Healthcare, Semiconductor Manufacturing, Industrial Automation, Retail, and Telecommunications domains. Strong experience in building enterprise Generative AI applications using Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and Prompt Engineering to automate business processes and improve decision-making. Hands-on experience in developing intelligent AI assistants using GPT-4, Claude, Gemini, Llama 3, LangChain, LangGraph, CrewAI, and AutoGen for document intelligence, enterprise search, workflow automation, and knowledge management. Experience in designing RAG solutions using Pinecone, FAISS, ChromaDB, vector embeddings, semantic search, hybrid search, and metadata filtering to improve information retrieval across enterprise knowledge repositories. Strong knowledge of prompt engineering techniques including few-shot prompting, role prompting, chain-of-thought prompting, tool calling, structured prompting, and context engineering to improve response quality and reduce hallucinations. Experience in fine-tuning Large Language Models using LoRA, PEFT, and Hugging Face Transformers to develop domain-specific AI solutions for banking, healthcare, and enterprise business applications. Hands-on experience in developing NLP applications using BERT, ClinicalBERT, BioBERT, spaCy, NLTK, and Hugging Face Transformers for document classification, named entity recognition, document summarization, semantic search, and information extraction. Strong experience in building Machine Learning and Deep Learning models using Scikit-learn, TensorFlow, PyTorch, XGBoost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machines (SVM) for predictive analytics and business intelligence. Experience in processing structured and unstructured enterprise data using Apache Spark, PySpark, Databricks, Apache Airflow, SQL, Pandas, and NumPy to support scalable AI and Machine Learning solutions. Hands-on experience in designing and deploying AI applications using AWS Bedrock, Amazon SageMaker, AWS Lambda, Amazon S3, Google Vertex AI, Docker, Kubernetes, FastAPI, and REST APIs. Strong experience in implementing MLOps and LLMOps practices using MLflow, DVC, GitHub Actions, Jenkins, Docker, Kubernetes, and CI/CD pipelines to automate model training, deployment, monitoring, and version management. Experience in evaluating AI systems using RAGAS, DeepEval, SHAP, and LIME to improve model quality, explainability, and enterprise AI reliability. Strong knowledge of enterprise AI security using RBAC, IAM, OAuth 2.0, encryption, AI Governance, Responsible AI, and HIPAA-compliant deployment practices. Experience in developing interactive dashboards and business reports using Tableau, Tableau Server, and advanced SQL to provide meaningful business insights and operational visibility. Strong experience in monitoring production AI applications, resolving model performance issues, improving inference latency, and maintaining highly available enterprise AI platforms. Worked closely with data scientists, software engineers, product owners, business analysts, DevOps teams, cloud engineers, and business stakeholders to deliver scalable AI solutions aligned with business objectives. Experience in participating in Agile Scrum ceremonies including sprint planning, backlog refinement, code reviews, release planning, production deployments, and post-production support. Proven ability to translate complex business requirements into practical AI and data-driven solutions that improve operational efficiency, reduce manual effort, and enhance customer experience. Strong analytical, problem-solving, communication, and collaboration skills with the ability to work effectively in fast-paced enterprise environments. Passionate about learning emerging AI technologies and delivering secure, scalable, and production-ready AI solutions that provide measurable business value. TECHNICAL SKILLS Category Technologies Programming Languages Python, SQL, PySpark Generative AI & Agentic AI GPT-4, Claude, Gemini, Llama 3, LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, AI Agents, Prompt Engineering, Tool Calling Large Language Models (LLMs) OpenAI GPT, Llama, Mistral, Hugging Face Transformers, LoRA, PEFT RAG & Vector Databases Retrieval-Augmented Generation (RAG), Pinecone, FAISS, ChromaDB, Vector Search, Semantic Search, Hybrid Search, Embeddings, Context Engineering Machine Learning Scikit-learn, XGBoost, Random Forest, Gradient Boosting, Logistic Regression, Decision Trees, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Na ve Bayes Deep Learning TensorFlow, PyTorch, Keras, CNN, RNN, LSTM Natural Language Processing (NLP) BERT, ClinicalBERT, BioBERT, spaCy, NLTK, Hugging Face Transformers, Named Entity Recognition (NER), Text Classification, Document Summarization, Sentiment Analysis Data Science & Analytics Pandas, NumPy, SciPy, Statistical Analysis, Feature Engineering, Feature Selection, Exploratory Data Analysis (EDA), Model Evaluation, A/B Testing MLOps & LLMOps MLflow, DVC, Docker, Kubernetes (EKS), GitHub Actions, Jenkins, CI/CD, Model Monitoring, Model Versioning, Prompt Evaluation, RAGAS, DeepEval Cloud Platforms AWS Bedrock, Amazon SageMaker, AWS Lambda, Amazon EC2, Amazon S3, Amazon EKS, Amazon RDS, AWS IAM, AWS CloudWatch, Google Vertex AI, Azure AI Services Data Engineering Apache Spark, PySpark, Databricks, Delta Lake, Apache Airflow, Apache Kafka, Hadoop, Hive, ETL Pipelines Databases PostgreSQL, Oracle, SQL Server, MySQL, Teradata, MongoDB, Redis API Development FastAPI, Flask, REST APIs, GraphQL, Microservices Visualization & BI Tableau, Tableau Server, Power BI, Matplotlib, Seaborn Security & Governance RBAC, OAuth 2.0, AWS IAM, AWS Secrets Manager, HashiCorp Vault, HIPAA, AI Governance, Responsible AI Monitoring & Observability Prometheus, Grafana, Amazon CloudWatch, ELK Stack Version Control & IDEs Git, GitHub, Jupyter Notebook, VS Code, PyCharm Methodologies Agile Scrum, SDLC, CRISP-DM, MLOps, LLMOps PROFESSIONAL WORK EXPERIENCE Client: Wells Fargo, McLean, VA / Mar 2025 Present Role: Gen AI Engineer Responsibilities Developed AI assistants that helped loan officers quickly search lending policies, underwriting guidelines, and compliance documents from enterprise knowledge repositories. Created intelligent document review solutions that automatically extracted important information from mortgage applications, financial statements, and customer documents before manual verification. Built Agentic AI workflows that coordinated multiple AI agents to validate customer information, review supporting documents, perform policy checks, and recommend the next business action. Improved customer service by developing AI-powered virtual assistants that answered banking questions, explained products, and guided customers through digital banking services. Built enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, Pinecone, and FAISS, enabling business users to retrieve accurate information from millions of banking documents. Improved document retrieval accuracy by optimizing chunk size, embedding strategies, metadata filtering, and hybrid search techniques, helping business users locate information more quickly. Applied prompt engineering techniques including few-shot prompting, role prompting, tool calling, and structured prompts to improve AI response quality and reduce hallucinations. Fine-tuned open-source language models using LoRA and PEFT for loan processing, fraud investigation, regulatory compliance, and customer service applications. Developed secure AI APIs using FastAPI that integrated enterprise AI services with internal banking applications and customer support platforms. Integrated AI applications with AWS Bedrock, Claude, GPT-4, Gemini, and Llama models, allowing business teams to choose the most suitable model for different banking use cases. Built reusable prompt libraries, AI tools, and agent templates that reduced development effort and accelerated delivery across multiple banking projects. Developed evaluation pipelines using RAGAS and DeepEval to measure answer quality, retrieval accuracy, and overall AI system performance before production deployment. Implemented AI Guardrails, RBAC, IAM policies, encryption, and secure API authentication to protect sensitive financial information and support regulatory compliance. Established LLMOps pipelines using MLflow, Docker, Kubernetes, GitHub Actions, and DVC, simplifying model deployment and lifecycle management. Built monitoring dashboards using Prometheus, Grafana, and CloudWatch to track model latency, token usage, infrastructure health, and API performance. Improved fraud investigation efficiency by providing investigators with AI-generated summaries, related transactions, and supporting regulatory references during case reviews. Supported production AI applications by investigating prompt failures, API issues, retrieval errors, and model performance problems, implementing permanent fixes to improve system stability. Worked closely with lending teams, compliance officers, fraud analysts, product owners, and software engineers to validate AI recommendations before production implementation. Participated in architecture discussions, sprint planning, code reviews, release activities, and production deployments while working in Agile Scrum teams. Prepared solution architecture documents, AI governance standards, operational runbooks, deployment guides, and support documentation for long-term maintenance. Improved enterprise knowledge search by reducing manual document review effort and helping business users complete critical banking tasks more efficiently. Evaluated new Generative AI frameworks, foundation models, and enterprise AI tools to improve scalability, maintainability, and long-term solution performance. Mentored junior engineers on Agentic AI, LLMOps, prompt engineering, RAG architectures, secure AI development, and enterprise deployment best practices. Collaborated with DevOps and Cloud teams to automate deployments, improve platform reliability, and support high availability across production environments. Delivered scalable AI solutions that reduced manual effort, improved business productivity, and enhanced customer experience across multiple banking operations. Environment: Python, GPT-4, Claude, Gemini, Llama 3, AWS Bedrock, LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, RAG, Pinecone, FAISS, ChromaDB, FastAPI, MLflow, DVC, Docker, Kubernetes (EKS), GitHub Actions, Prometheus, Grafana, CloudWatch, RAGAS, DeepEval, LoRA, PEFT, AWS Lambda, Amazon S3, IAM, KMS, OAuth 2.0, RBAC, REST APIs, Agile Scrum. Client: Kaiser Permanente, Oakland, CA / Aug 2022 Feb 2025 Role: Senior AI/ML Engineer Responsibilities: Supported physicians by providing AI-driven clinical recommendations based on patient history, diagnosis, laboratory results, and treatment guidelines. Helped care managers locate medical policies, clinical procedures, and patient education materials within seconds using enterprise search solutions. Reduced manual review of patient records by automatically summarizing physician notes, discharge summaries, and clinical documentation before specialist review. Assisted prior authorization teams by retrieving supporting clinical evidence and presenting the most relevant healthcare information for faster approval decisions. Enabled healthcare professionals to ask medical questions in natural language and receive accurate responses from enterprise knowledge repositories using Retrieval-Augmented Generation (RAG). Processed structured and unstructured healthcare information from Electronic Health Records (EHR), laboratory systems, and pharmacy applications for AI model development. Extracted diagnoses, medications, ICD codes, CPT codes, procedures, allergies, and clinical observations from healthcare documents using ClinicalBERT, spaCy, and Hugging Face Transformers. Reduced document search time for clinicians by improving semantic retrieval using optimized embeddings, metadata filtering, and vector search techniques. Automated clinical document classification, medical coding support, and healthcare document routing using transformer-based NLP models. Improved healthcare response quality by evaluating prompts, refining context retrieval, and validating AI-generated recommendations before production deployment. Healthcare datasets from EHR systems, pharmacy platforms, and laboratory applications were standardized before model training, improving prediction reliability. Established automated ML pipelines using Amazon SageMaker, MLflow, and GitHub Actions, reducing deployment effort and improving model release consistency. Deployed secure AI services through FastAPI and Docker containers, allowing healthcare applications to consume prediction services with minimal latency. Protected patient information by implementing encryption, IAM policies, RBAC, and secure access controls while supporting HIPAA compliance requirements. Measured model quality using precision, recall, F1-score, SHAP, and LIME to improve prediction reliability and provide explainable healthcare insights. Tracked production model health using CloudWatch, Grafana, and Prometheus, helping support teams identify issues before they affected healthcare users. Worked with physicians, pharmacists, nurses, healthcare analysts, and compliance teams to verify AI recommendations before production rollout. Resolved production issues related to model inference, API failures, data quality, and pipeline execution, improving overall application stability. Participated in sprint planning, technical discussions, production releases, and knowledge-sharing sessions while working in Agile Scrum teams. Prepared architecture diagrams, deployment procedures, support documents, and operational guides for future enhancements. Reduced administrative effort for healthcare teams by automating repetitive clinical document processing and information retrieval activities. Evaluated new healthcare AI models and NLP techniques to improve clinical search, prediction accuracy, and overall platform performance. Assisted DevOps teams in automating deployments, monitoring cloud infrastructure, and maintaining highly available AI services. Shared technical knowledge with junior engineers on healthcare AI, NLP, production support, and enterprise development practices. Delivered AI solutions that improved patient care workflows, reduced operational effort, and supported faster clinical decision-making across multiple healthcare departments. Environment: Python, Generative AI, Large Language Models (LLMs), Hugging Face Transformers, ClinicalBERT, BioBERT, LangChain, Retrieval-Augmented Generation (RAG), LangGraph, Pinecone, FAISS, spaCy, FastAPI, TensorFlow, PyTorch, Scikit-learn, XGBoost, Apache Spark, PySpark, Databricks, Apache Airflow, Amazon SageMaker, MLflow, Docker, Kubernetes (Amazon EKS), GitHub Actions, AWS Lambda, Amazon S3, PostgreSQL, Redis, SHAP, LIME, Tableau, Prometheus, Grafana, Amazon CloudWatch, AWS IAM, AWS Secrets Manager, HashiCorp Vault, OAuth 2.0, RBAC, REST APIs, HIPAA, AI Governance, Responsible AI, Agile Scrum. Client: Intel, | Santa Clara, CA/ Apr 2020 Jul 2022 Role: Data Scientist Responsibilities: Analyzed semiconductor manufacturing data to identify production bottlenecks, quality issues, and equipment performance trends across fabrication facilities. Predicted equipment failures using machine learning models, allowing maintenance teams to schedule repairs before unexpected production interruptions occurred. Monitored wafer production metrics and process stability to identify abnormal manufacturing conditions at an early stage. Examined sensor readings collected from manufacturing equipment to recognize operating patterns and reduce unplanned downtime. Forecasted production capacity using historical manufacturing data, helping operations teams prepare for changing business demand. Evaluated process yield by comparing equipment performance across multiple production lines and identifying opportunities to improve manufacturing efficiency. Processed high-volume manufacturing datasets using PySpark, Apache Spark, and Spark SQL, preparing reliable analytical data for machine learning models. Applied feature engineering techniques including scaling, normalization, missing value handling, and feature selection to improve model performance. Trained predictive models using Random Forest, XGBoost, Gradient Boosting, Logistic Regression, and Support Vector Machine (SVM) for equipment reliability and defect prediction. Compared multiple machine learning algorithms through cross-validation and statistical evaluation to identify the best-performing production models. Measured prediction accuracy using RMSE, Precision, Recall, F1-Score, ROC-AUC, and MAE before releasing models into production. Optimized SQL queries in Teradata and Spark SQL, reducing data processing time for daily manufacturing reports. Automated recurring analytical workflows using Python, decreasing manual effort required for production reporting. Published Tableau dashboards that displayed manufacturing KPIs, equipment utilization, production throughput, and quality metrics for engineering leadership. Investigated data quality issues by validating incoming production data and correcting inconsistencies before model training. Coordinated with manufacturing engineers, quality assurance teams, process engineers, and production managers to verify analytical findings and business recommendations. Resolved production issues affecting analytical pipelines, scheduled jobs, and reporting processes, improving platform stability. Participated in release planning, sprint reviews, backlog refinement, and technical discussions while working in Agile Scrum teams. Documented machine learning workflows, feature engineering standards, validation procedures, and operational guidelines to support future enhancements. Reduced false equipment alerts by refining prediction models and improving feature selection techniques used across manufacturing systems. Increased production visibility by delivering dashboards that enabled engineering teams to monitor critical manufacturing processes in near real time. Reviewed newly released machine learning libraries and analytical techniques to improve prediction quality and simplify future model maintenance. Shared analytical approaches and model evaluation techniques with junior data scientists during technical knowledge-sharing sessions. Worked closely with infrastructure teams to maintain reliable analytical environments and support production deployments. Delivered predictive analytics solutions that improved manufacturing efficiency, reduced operational risks, and supported better production planning decisions. Environment: Python, Apache Spark, PySpark, Spark SQL, Pandas, NumPy, Scikit-learn, XGBoost, Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM), Decision Trees, MLlib, Teradata, SQL, Tableau, Tableau Server, Alteryx, Apache Hadoop, MapReduce, Git, Jupyter Notebook, Linux, AWS, Agile Scrum. Client: Emerson, St. Louis, MO / Jan 2018 Mar 2020 Role: Data Scientist Responsibilities: Collected sensor readings from industrial equipment to identify changes in machine performance and operating conditions across manufacturing plants. Detected unusual equipment behavior by analyzing temperature, pressure, vibration, and operational measurements before failures occurred. Tracked production efficiency using historical operational data, allowing plant managers to make better maintenance and scheduling decisions. Identified production trends by studying machine utilization, equipment availability, and factory performance across multiple manufacturing lines. Estimated equipment maintenance schedules using historical sensor information, helping maintenance teams reduce unexpected production interruptions. Prepared manufacturing datasets from IoT devices, PLC systems, and production databases for machine learning and statistical analysis. Applied feature extraction and data transformation techniques using Python, Pandas, and NumPy to improve prediction quality. Utilized Random Forest, XGBoost, Decision Trees, and Support Vector Machine (SVM) algorithms to estimate equipment health and production reliability. Assessed model performance using Precision, Recall, ROC-AUC, F1-Score, RMSE, and MAE before production implementation. Organized large manufacturing datasets using Apache Spark and PySpark, improving processing efficiency for enterprise analytical workloads. Exposed prediction services through Flask REST APIs, allowing industrial applications to access machine learning results in real time. Scheduled automated model retraining and deployment activities using AWS SageMaker and Jenkins, reducing manual operational effort. Packaged machine learning applications using Docker, ensuring consistent deployments across development, testing, and production environments. Business leaders used Tableau dashboards to monitor production output, equipment utilization, and manufacturing KPIs across multiple plants. Investigated data inconsistencies by validating sensor readings and production records before analytical processing, improving overall data reliability. Coordinated with production engineers, maintenance teams, automation specialists, and plant managers to validate analytical findings before operational rollout. Responded to production incidents involving prediction services, data pipelines, and reporting applications, minimizing disruption to manufacturing operations. Participated in sprint planning, technical reviews, release activities, and Agile ceremonies while working with cross-functional engineering teams. Prepared technical standards, operational procedures, deployment documents, and troubleshooting guides for long-term application support. Increased equipment availability by providing maintenance teams with early visibility into machine performance and operational risks. Reduced manual monitoring by automating equipment health analysis and predictive maintenance reporting across manufacturing facilities. Reviewed newly available machine learning techniques and industrial analytics approaches to improve prediction quality and operational efficiency. Assisted junior team members with data preparation, model validation, production troubleshooting, and analytical best practices. Worked alongside infrastructure teams to maintain stable analytical environments and support production deployments across manufacturing systems. Delivered predictive analytics solutions that improved factory efficiency, reduced maintenance costs, and supported continuous process improvement initiatives. Environment: Python, Pandas, NumPy, Scikit-learn, Apache Spark, PySpark, XGBoost, Random Forest, Decision Trees, Support Vector Machine (SVM), Flask, Docker, AWS SageMaker, Jenkins, SQL, Oracle, SQL Server, Tableau, Tableau Server, Git, Linux, Jupyter Notebook, Agile Scrum. Client: Lowe's, Mooresville, NC / Oct 2015 Dec 2017 Role: Sr. Data Analyst Responsibilities: Reviewed daily sales performance across multiple retail stores to identify revenue trends and product demand across different regions. Compared inventory levels with customer demand to recommend replenishment plans and reduce stock shortages. Prepared analytical datasets using Python, SQL, Pandas, and NumPy to support sales reporting and business analysis. Designed interactive Tableau dashboards that helped business users monitor sales, inventory movement, and operational KPIs. Examined customer purchasing patterns to support merchandising teams in planning seasonal promotions and product placement. Optimized SQL queries and stored procedures to improve report execution time and provide faster access to business data. Automated recurring sales and inventory reports using Python, reducing manual reporting effort and improving report delivery. Validated business data from multiple enterprise systems using SQL and ETL processes before publishing reports, improving reporting accuracy. Analyzed pricing trends and promotional performance to support business decisions related to product pricing and discount strategies. Worked with Oracle, SQL Server, and Hadoop datasets to consolidate retail information from multiple business applications. Built reusable reporting templates in Tableau that simplified performance tracking for merchandising and supply chain teams. Investigated reporting issues by tracing data inconsistencies and working with Data Engineering teams to resolve source data problems. Assisted User Acceptance Testing (UAT) by validating report calculations, dashboard metrics, and business rules before production deployment. Coordinated with merchandising managers, finance teams, supply chain analysts, and store operations to deliver accurate analytical reports. Documented reporting logic, SQL scripts, dashboard calculations, and business definitions for future maintenance. Improved reporting efficiency by streamlining ETL workflows and reducing duplicate data processing activities. Supported production reporting by resolving dashboard issues, correcting data discrepancies, and ensuring reports were delivered on schedule. Participated in sprint planning, requirement discussions, report enhancements, and Agile ceremonies while working with cross-functional teams. Shared reporting knowledge and SQL best practices with junior analysts, improving team productivity and reporting consistency. Delivered business insights that helped improve inventory planning, sales forecasting, and overall retail decision-making. Environment: Python, SQL, Pandas, NumPy, Tableau, Tableau Server, Oracle, SQL Server, Hadoop, Hive, MongoDB, Alteryx, ETL, Microsoft Excel, Git, ER Studio, JIRA, Agile Scrum. Client: Charter Communications, Stamford, CT / Jul 2014 Sep 2015 Role: Data Analyst Responsibilities: Gathered customer subscription data from multiple business systems to prepare reports for customer service, marketing, and operations teams. Compiled daily operational reports to monitor subscriber growth, service requests, and network performance across different regions. Organized telecom datasets using Hive, HDFS, and SQL, making business information available for reporting and analysis. Applied data cleansing and preprocessing techniques using Python, Pandas, and NumPy to improve data consistency before report generation. Prepared customer usage reports that helped business teams understand subscription trends and service utilization. Produced interactive dashboards using Tableau Desktop to present subscriber statistics, operational KPIs, and service performance to management. Customized Tableau visualizations using calculated fields, parameters, filters, and dashboard actions to improve report usability. Retrieved business information by writing SQL and HiveQL queries against enterprise data warehouses and Hadoop platforms. Published dashboards to Tableau Server and managed scheduled data refresh activities to ensure reports remained current. Examined customer usage patterns using K-Means Clustering to support marketing campaigns and customer segmentation initiatives. Verified reporting accuracy by validating source data and resolving data quality issues before reports were shared with business users. Assisted customer support teams by preparing ad hoc reports related to service requests, customer complaints, and account activity. Worked with Data Engineering teams to improve data integration processes and maintain reliable reporting datasets. Participated in User Acceptance Testing (UAT) by validating report outputs, dashboard calculations, and business requirements before production releases. Recorded reporting procedures, dashboard specifications, and business definitions to support future report enhancements. Attended sprint planning sessions, requirement discussions, and project review meetings while working in Agile Scrum teams. Maintained reporting schedules and monitored dashboard refresh jobs, ensuring business users received timely information. Resolved reporting issues by identifying missing data, correcting calculation errors, and coordinating fixes with technical teams. Shared analytical findings with marketing managers, customer service teams, and business stakeholders to support operational decision-making. Contributed to reporting improvements that enhanced data visibility, increased reporting accuracy, and supported better business planning. Environment: Python, SQL, Hive, HiveQL, Hadoop, HDFS, MapReduce, Pandas, NumPy, Scikit-learn, K-Means Clustering, Tableau Desktop, Tableau Server, Microsoft Excel, Git, JIRA, Agile Scrum. Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree active directory California Connecticut Missouri North Carolina Virginia