Available to hire
I am Govardhan R, an AI/ML and Generative AI engineer with 5+ years of hands-on experience and 3+ years focused on Generative AI. I design and deploy applications powered by large language models, including intelligent chatbots, text summarizers, and document processing systems. I hold a strong foundation in OOP/OOD, data structures, algorithms, and design patterns, with extensive experience in Python, TensorFlow, PyTorch, and Keras. I build end-to-end ML pipelines using GPT-3.5, LLaMA-2, LangChain, Hugging Face Transformers, and FAISS, and I have deployed production-grade services on Azure Machine Learning, Docker, and AKS.
I excel at prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), and MLOps. I design transparent, testable prompts and model behaviors, package and test AI models with Conda and Jupyter, and ensure reproducible cloud deployments. I bring strong engineering discipline together with practical insights into model performance, collaborating with business and technical teams to deliver reliable AI capabilities while adhering to governance and transparency standards.
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Language
Bashkir
Advanced
Javanese
Intermediate
English
Fluent
Work Experience
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentFine-tuned GPT-3.5 for domain-specific chatbot applications integrated with backend systems via FastAPI. Designed advanced prompt templates and applied cutting-edge prompt engineering techniques to enhance LLM performance and business alignment. Built a Retrieval-Augmented Generation pipeline using FAISS and Chroma. Deployed and monitored LLM services on Azure Machine Learning, managing training data via Azure Blob Storage. Developed LLM-driven workflows with LangChain incorporating tool-based reasoning, memory, and custom agents. Applied LoRA techniques to fine-tune LLaMA-2 models with Hugging Face and PEFT. Containerized services with Docker and deployed via Azure Kubernetes Service. Used Weights & Biases for experiment tracking and designed MLOps pipelines to automate model lifecycle. Created interactive dashboards for model performance metrics visualization.
AI/ML Engineer at Omnicell
May 1, 2024 - August 5, 2025Designed scalable ML pipelines using AWS SageMaker, scikit-learn, and XGBoost for data preprocessing through deployment. Developed ETL workflows automating data extraction, transformation, and loading with AWS Glue, pandas, and SQL. Built supervised models with LightGBM, PyTorch, and TensorFlow tuned via SageMaker Hyperparameter Tuner. Implemented real-time inference APIs using FastAPI deployed as AWS Lambda functions. Managed batch inference and monitored models with tools like CloudWatch and MLflow. Leveraged AWS SageMaker Feature Store and automated infrastructure provisioning with Terraform and CloudFormation. Developed NLP solutions with Hugging Face, spaCy, and NLTK and ensured secure deployment workflows. Conducted model explainability and fairness analysis, collaborated cross-functionally using AWS native tools, and maintained CI/CD pipelines.
ML Engineer at TCS
December 1, 2022 - August 5, 2025Developed end-to-end ML workflows using Azure Machine Learning Pipelines automating preprocessing, training, and deployment. Created reusable Python scripts and Jupyter Notebooks for data exploration and feature engineering. Monitored deployed models with Azure Application Insights and optimized hyperparameters with scikit-learn. Deployed RESTful endpoints with Azure ML Endpoints integrated with FastAPI. Built Power BI dashboards for model reporting, designed data pipelines with Azure Data Factory, and implemented data validation with Great Expectations. Used Azure ML Designer to create reusable ML components and managed code with Git and Azure DevOps for CI/CD automation.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentFine-tuned GPT-3.5 using the OpenAI API for a domain-specific chatbot application integrated with backend systems via FastAPI. Designed and optimized prompt templates employing few-shot learning, chain-of-thought prompting, and role-based instructions to enhance LLM accuracy aligned to business goals. Built Retrieval-Augmented Generation (RAG) pipelines using FAISS and Chroma for context-aware document retrieval. Deployed and monitored LLM services on Azure Machine Learning, utilizing Azure ML Pipelines and Model Registry for scalable training and deployment. Managed training data and model checkpoints in Azure Blob Storage. Developed LLM-driven workflows with LangChain to support tool-based reasoning, memory, and custom agents. Applied LoRA techniques to fine-tune LLaMA-2 models in PyTorch. Containerized GenAI services with Docker and deployed inference endpoints on Azure Kubernetes Service. Tracked experiments and model performance using Weights & Biases. Created semantic search util
AI/ML Engineer at Omnicell
May 1, 2024 - August 5, 2025Designed and deployed scalable machine learning pipelines using AWS SageMaker, scikit-learn, and XGBoost covering data preprocessing through to deployment. Developed ETL workflows automating data extraction from S3 and loading into Redshift and RDS using AWS Glue, pandas, and SQL. Built supervised models for classification, regression, and forecasting with LightGBM, PyTorch, and TensorFlow combined with hyperparameter tuning on SageMaker. Implemented real-time inference APIs with FastAPI deployed as AWS Lambda functions for low-latency predictions. Deployed batch inference pipelines using AWS Batch and Step Functions. Monitored model metrics using CloudWatch, SageMaker Model Monitor, and MLflow integrated with alerting for drift detection. Leveraged SageMaker Feature Store for feature management. Automated infrastructure deployment with Terraform, CloudFormation, and CodePipeline enabling reproducible MLOps. Managed large datasets on AWS optimized for cost and security. Developed NLP s
ML Engineer at TCS
December 1, 2022 - August 5, 2025Developed end-to-end machine learning workflows using Azure Machine Learning Pipelines automating data preprocessing, training, and deployment. Created reusable Python scripts and Jupyter Notebooks for data exploration, feature engineering, and outlier detection using pandas and NumPy. Monitored deployed models with Azure Application Insights including real-time inference metrics and logging. Performed hyperparameter optimization using GridSearchCV and RandomizedSearchCV improving model generalization. Deployed models as RESTful endpoints via Azure ML Endpoints integrated with FastAPI and Swagger. Built dashboards with Power BI for stakeholder reporting of model metrics and feature importance. Designed version-controlled data pipelines via Azure Data Factory for scheduled ingestion and transformations. Implemented data validation using Great Expectations to ensure data quality. Created reusable ML components in Azure ML Designer for pipeline stages. Used Git and Azure DevOps for versio
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentFine-tuned GPT-3.5 for a domain-specific chatbot and integrated backend systems using FastAPI microservices. Developed and optimized prompt templates and applied advanced prompt engineering techniques to improve model accuracy and alignment. Built Retrieval-Augmented Generation pipelines using FAISS and Chroma for contextual document retrieval. Deployed LLM services on Azure Machine Learning with scalable pipelines and Azure Kubernetes Service. Managed data and model artifacts in Azure Blob Storage. Used LangChain for custom agent workflows and applied LoRA fine-tuning techniques on LLaMA-2 models. Implemented CI/CD workflows with Git and Azure DevOps, tracked experiments with Weights & Biases, and created data visualizations with Plotly. Collaborated in Agile/SAFe environments with cross-functional teams, documented AI models for compliance, and integrated AI outputs into business workflows.
AI/ML Engineer Intern at Omnicell
May 1, 2024 - August 20, 2025Designed and deployed scalable ML pipelines on AWS SageMaker using scikit-learn and XGBoost. Automated ETL workflows with AWS Glue, pandas, and SQL, managing data in S3, Redshift, and RDS. Built models for classification, regression, and forecasting with LightGBM, PyTorch, and TensorFlow. Developed real-time inference APIs using FastAPI, containerized with Docker, deployed as AWS Lambda functions. Monitored model metrics with CloudWatch and SageMaker Model Monitor. Managed features with SageMaker Feature Store. Automated deployments with Terraform, CloudFormation, and CI/CD pipelines. Secured workflows with IAM roles and encryption. Conducted explainability and fairness analysis using SHAP and LIME. Developed NLP solutions with Hugging Face Transformers and deployed in SageMaker endpoints. Collaborated cross-functionally to integrate ML into operational tools for automation and optimization.
ML Engineer at TCS
December 1, 2022 - August 20, 2025Developed end-to-end ML workflows using Azure Machine Learning Pipelines for data preprocessing, training, and model deployment. Applied OOP and design patterns for scalable ML solutions. Created reusable Python scripts for feature engineering and outlier detection. Monitored models with Azure Application Insights and logged inference metrics. Optimized hyperparameters with GridSearchCV and RandomizedSearchCV. Deployed RESTful model endpoints with FastAPI. Built Power BI dashboards for performance visualization. Designed version-controlled data pipelines using Azure Data Factory. Implemented data validation checks using Great Expectations. Created reusable ML components in Azure ML Designer. Managed CI/CD with Git and Azure DevOps in Agile/SAFe environments.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentFine-tuned GPT-3.5 for a domain-specific chatbot integrated with backend systems using FastAPI microservices. Designed and optimized prompt templates improving LLM accuracy through advanced techniques such as few-shot learning and chain-of-thought prompting. Built a Retrieval-Augmented Generation pipeline using FAISS and Chroma, deployed and monitored LLM-based services on Azure Machine Learning, managing data and checkpoints with Azure Blob Storage. Developed workflows with LangChain, applied LoRA techniques to fine-tune LLaMA-2 models, containerized services with Docker, and deployed on Azure Kubernetes Service. Led experiment tracking via Weights & Biases and implemented MLOps pipelines with Git and Azure DevOps in an Agile/SAFe environment. Collaborated with stakeholders to integrate AI outputs into business workflows and documented model assumptions for compliance.
AI/ML Engineer Intern at Omnicell
May 1, 2024 - August 26, 2025Developed scalable ML pipelines using AWS SageMaker and popular ML frameworks, automating data preprocessing, training, and deployment. Created ETL workflows utilizing AWS Glue, pandas, and SQL for data ingestion. Implemented real-time inference APIs with FastAPI and AWS Lambda, and batch pipelines using AWS Batch and Step Functions. Monitored model metrics with CloudWatch and SageMaker Model Monitor, managed features via SageMaker Feature Store, and automated infrastructure with Terraform and CloudFormation. Developed NLP solutions with Hugging Face, spaCy, and NLTK, conducted model explainability with SHAP and LIME, and maintained CI/CD pipelines with AWS CodeBuild, CodeCommit, and CodeDeploy. Ensured security and compliance through AWS IAM, KMS encryption, and private buckets.
ML Engineer at TCS
December 1, 2022 - August 26, 2025Designed end-to-end ML workflows using Azure Machine Learning Pipelines for automating data preprocessing, training, and deployments. Applied OOP/OOD patterns and algorithmic optimizations for scalable workflows. Created reusable scripts for data exploration and feature engineering, monitored deployed models with Azure Application Insights, and optimized hyperparameters using GridSearchCV and RandomizedSearchCV. Deployed models as RESTful endpoints via Azure ML Endpoints with FastAPI. Built dashboards for performance visualization using Power BI. Implemented data validation with Great Expectations and reusable ML components in Azure ML Designer. Used Git and Azure DevOps for version control and CI/CD automation in Agile/SAFe environments.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentLed the fine-tuning of GPT-3.5 for domain-specific chatbot applications, integrating backend systems via FastAPI microservices. Designed prompt templates and applied advanced prompt engineering techniques to enhance large language model accuracy. Developed Retrieval-Augmented Generation pipelines using FAISS and Chroma for context-aware document retrieval. Deployed and monitored AI services on Azure Machine Learning and Azure Kubernetes Service. Specialized in model fine-tuning with LoRA, experiment tracking with Weights & Biases, and creating MLOps CI/CD workflows using Git and Azure DevOps. Delivered interactive dashboards for performance metrics and collaborated cross-functionally to refine AI systems in Agile/SAFe environments, ensuring transparency and AI governance compliance.
AI/ML Engineer Intern at Omnicell
May 1, 2024 - September 4, 2025Designed and deployed scalable ML pipelines using AWS SageMaker with frameworks such as scikit-learn and XGBoost. Developed ETL workflows and automated data processing for feature engineering. Built supervised models including linear/logistic regression and ensemble methods, deployed real-time and batch inference APIs using FastAPI and AWS Lambda. Monitored models with CloudWatch and SageMaker Model Monitor. Implemented MLOps automation with Terraform and AWS CI/CD tools, secured deployment workflows, and performed explainability and fairness analysis. Used Jupyter and Conda for reproducible experimentation and collaborated cross-functionally to integrate ML outputs into operational tools for manufacturing optimization.
ML Engineer at Tata Consultancy Services (TCS)
December 1, 2022 - September 4, 2025Developed end-to-end machine learning workflows using Azure Machine Learning Pipelines including data preprocessing, training, and deployment. Applied software design principles such as OOP/OOD and optimized hyperparameters using scikit-learn. Deployed RESTful endpoints with FastAPI, monitored and logged inference metrics with Azure Application Insights, and built Power BI dashboards. Created version-controlled data pipelines with Azure Data Factory and ensured data quality via Great Expectations. Implemented CI/CD automation with Git and Azure DevOps in Agile/SAFe settings, and contributed reusable ML components and governance documentation to promote scalability and reproducibility.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - PresentFine-tuned GPT-3.5 for a domain-specific chatbot and integrated with backend systems through FastAPI. Designed prompts enabling few-shot learning and chain-of-thought reasoning. Built a retrieval-augmented generation pipeline using FAISS and Chroma for context-aware document retrieval. Deployed LLM services on Azure Machine Learning with pipelines, compute instances, and model registry. Managed data and checkpoints in Azure Blob Storage. Developed LangChain-based workflows for tool-based reasoning, memory, and custom agents. Applied LoRA to fine-tune LLaMA-2 with PEFT in PyTorch. Containerized services with Docker and exposed inference endpoints via AKS. Tracked experiments with Weights & Biases and created semantic search utilities in Chroma for fast retrieval. Implemented MLOps pipelines with Git, Azure DevOps, and CI/CD; built dashboards with Plotly to monitor performance.
AI/ML Engineer at Omnicell
May 1, 2024 - October 22, 2025Designed and deployed scalable ML pipelines using AWS SageMaker, scikit-learn, and XGBoost; automated data preprocessing, training, evaluation, and deployment. Built NLP solutions with Hugging Face Transformers, spaCy, and NLTK; deployed endpoints on SageMaker. Implemented real-time inference APIs with FastAPI, Docker, and AWS Lambda behind API Gateway, and batch inference with AWS Batch and Step Functions. Monitored model metrics with CloudWatch and SageMaker Model Monitor; used MLflow for experiment tracking. Leveraged SageMaker Feature Store to reuse features; automated infrastructure with Terraform and CodePipeline. Managed large datasets with S3, Glue Data Catalog, and Lake Formation. Created CI/CD pipelines with CodeBuild, CodeCommit, and CodeDeploy; ensured secure deployments with IAM, KMS, and VPC endpoints. Conducted explainability and fairness analysis with SHAP, LIME, and Fairlearn; dashboards with Streamlit on EC2.
ML Engineer at TCS
December 1, 2022 - October 22, 2025Developed end-to-end ML workflows using Azure Machine Learning Pipelines; automated preprocessing, training, evaluation, and deployment. Created reusable Python scripts and Jupyter Notebooks for data exploration and feature engineering; monitored models with Application Insights and logging. Optimized hyperparameters with GridSearchCV and RandomizedSearchCV; deployed models as REST endpoints via Azure ML Endpoints with FastAPI. Built dashboards with Power BI to visualize performance metrics. Designed data pipelines with Azure Data Factory and implemented data validation with Great Expectations. Created reusable ML components in Azure ML Designer and managed versioning with Git and Azure DevOps; implemented CI/CD automation.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - November 6, 2025Fine-tuned GPT-3.5 for a domain-specific chatbot, integrated with backend systems via FastAPI; designed prompt templates with few-shot, chain-of-thought, and role-based prompts; built a Retrieval-Augmented Generation (RAG) pipeline using FAISS with Chroma; deployed and monitored LLM services on Azure Machine Learning with ML Pipelines, Compute Instances, and Model Registry; managed training data and checkpoints in Azure Blob Storage; developed LangChain workflows for tool-based reasoning, memory, and custom agents; applied LoRA to fine-tune LLaMA-2 models using PEFT in PyTorch; containerized services with Docker and deployed endpoints on AKS; tracked experiments with Weights & Biases; created internal semantic search utilities with vector embeddings stored in Chroma; designed MLOps pipelines with Git, Azure DevOps, and CI/CD; built interactive dashboards with Plotly for model performance monitoring.
AI/ML Engineer at Omnicell
May 1, 2024 - May 1, 2024Designed and deployed scalable ML pipelines on AWS SageMaker, handling data preprocessing, training, evaluation, and model deployment; developed ETL workflows using AWS Glue, pandas, and SQL; built supervised models for classification, regression, and forecasting with LightGBM, PyTorch, and TensorFlow; hyperparameter tuning with SageMaker Hyperparameter Tuner; implemented real-time inference APIs via FastAPI, containerized with Docker, and deployed as AWS Lambda functions behind API Gateway; built batch inference pipelines with AWS Batch, Step Functions, and S3; monitored metrics with CloudWatch, SageMaker Model Monitor, and MLflow; leveraged SageMaker Feature Store to manage features across projects; automated infrastructure provisioning and deployment with Terraform, CloudFormation, and CodePipeline; managed large-scale datasets with S3, Glue Data Catalog, and Lake Formation; NLP solutions using Hugging Face Transformers, spaCy, and NLTK; queries using AWS Athena and Redshift Spectru
ML Engineer at TCS
December 1, 2022 - December 1, 2022Developed end-to-end machine learning workflows using Azure Machine Learning Pipelines; automated data preprocessing, training, and model deployment; created reusable Python scripts and notebooks for data exploration, feature engineering, and outlier detection with pandas and NumPy; monitored deployed models with Azure Application Insights and logging for real-time inference; optimized hyperparameters with GridSearchCV and RandomizedSearchCV; deployed models as RESTful endpoints via Azure ML Endpoints, enabling integration with client applications through FastAPI and Swagger; built dashboards with Power BI to visualize metrics and feature importance; designed version-controlled data pipelines with Azure Data Factory; implemented data validation with Great Expectations; created reusable ML components in Azure ML Designer; used Git and Azure DevOps for version control and CI/CD; built robust ML features deployment across environments.
Gen AI Engineer at Tri Counties Bank
June 1, 2024 - November 25, 2025Fine-tuned GPT-3.5 using the OpenAI API for a domain-specific chatbot, integrating with backend systems via FastAPI. Designed and optimized prompt templates for diverse user queries. Applied few-shot learning, chain-of-thought prompting, and role-based instructions to improve alignment. Built a Retrieval-Augmented Generation (RAG) pipeline using FAISS and integrated with Chroma for context-aware document retrieval. Deployed and monitored LLM services on Azure ML, including Pipelines, Compute Instances, and Model Registry; managed training data and model checkpoints on Azure Blob Storage. Developed LangChain-based workflows for tool-enabled reasoning and memory, and applied LoRA to fine-tune LLaMA-2 models via PEFT. Containerized services with Docker and deployed endpoints to AKS. Tracked experiments with Weights & Biases and built semantic search utilities with embeddings and Chroma. Designed MLOps pipelines with Git/CI/CD and visualized metrics with Plotly/Dash.
ML Engineer at TCS
December 31, 2022 - December 31, 2022Developed end-to-end ML workflows using Azure Machine Learning Pipelines, automating data preprocessing, training, and deployment. Created reusable Python scripts and Jupyter Notebooks for data exploration, feature engineering, and outlier detection with pandas and NumPy. Monitored deployed models with Azure Application Insights and logging. Optimized hyperparameters using GridSearchCV and RandomizedSearchCV to improve generalization. Deployed models as RESTful endpoints via Azure ML Endpoints and integrated with FastAPI. Built Power BI dashboards to visualize performance and feature importance. Designed version-controlled data pipelines with Azure Data Factory and implemented data validation with Great Expectations. Created reusable ML components in Azure ML Designer and used Git/Azure DevOps for CI/CD automation.
AI/ML Engineer at Omnicell
February 1, 2023 - May 1, 2024Designed and deployed scalable ML pipelines on AWS SageMaker using scikit-learn and XGBoost; automated ETL with AWS Glue; trained supervised models and implemented real-time inference APIs with FastAPI, Docker, and AWS Lambda; batch inference with AWS Batch and Step Functions; monitored metrics with CloudWatch, SageMaker Model Monitor, and MLflow; leveraged SageMaker Feature Store; automated infrastructure provisioning with Terraform, CloudFormation, and CodePipeline; managed large datasets with S3, Glue Data Catalog, and Lake Formation; built NLP solutions using Hugging Face Transformers, spaCy, and NLTK; queried data with AWS Athena and Redshift Spectrum; created CI/CD pipelines with AWS CodeBuild, CodeCommit, and CodeDeploy; secured deployments with IAM roles, KMS, and VPC endpoints; conducted explainability analyses with SHAP, LIME, and Fairlearn; dashboards via Streamlit on EC2.
ML Engineer at TCS
December 1, 2020 - December 1, 2022Developed end-to-end ML workflows using Azure Machine Learning Pipelines; performed feature engineering and hyperparameter tuning with GridSearchCV and RandomizedSearchCV; deployed models as REST endpoints via Azure ML Endpoints; visualization via Power BI; designed version-controlled data pipelines with Azure Data Factory; implemented data validation with Great Expectations; created reusable ML components in Azure ML Designer; leveraged Git and Azure DevOps for version control and CI/CD automation.
AI/ML Engineer Intern at Omnicell
February 1, 2023 - May 1, 2024Designed scalable ML pipelines using AWS SageMaker, scikit-learn, and XGBoost; implemented ETL workflows with AWS Glue, pandas, and SQL to process data from S3 into Redshift/RDS. Built supervised models (classification, regression, forecasting) with LightGBM, PyTorch, and TensorFlow, including hyperparameter tuning via SageMaker. Created real-time inference APIs with FastAPI, Docker, and AWS Lambda behind API Gateway; set up batch inference via AWS Batch, Step Functions, and S3. Monitored models with CloudWatch, SageMaker Model Monitor, and MLflow; leveraged SageMaker Feature Store for feature reuse. Automated infrastructure with Terraform/CloudFormation and CodePipeline. Implemented NLP solutions with Hugging Face Transformers, spaCy, and NLTK; supported data querying with AWS Athena/Redshift Spectrum. Built CI/CD pipelines for ML with CodeBuild/CodeCommit/CodeDeploy; ensured secure deployments with IAM, KMS, VPC, and private S3. Conducted model explainability using SHAP/LIME and dash
Education
Master of Science in Computer Science at Concordia University St. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University ST. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University ST. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University, St. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University St. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University Saint Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University, St. Paul
January 1, 2023 - May 1, 2024Master of Science in Computer Science at Concordia University, St. Paul
January 1, 2023 - May 1, 2024Qualifications
Industry Experience
Financial Services, Software & Internet, Healthcare, Professional Services, Other, Manufacturing, Computers & Electronics
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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