Available to hire
Hi! I’m Ravi Teja, a Senior AI/ML Engineer and Data Scientist with over 10 years of experience in designing and deploying scalable machine learning and GenAI solutions. I specialize in working with large language models like GPT-4 and LLaMA, as well as building end-to-end pipelines and deploying AI microservices across AWS, Azure, and GCP.
I enjoy collaborating with cross-functional teams to solve complex problems and deliver impactful AI-powered applications. I’m passionate about ethical AI, model optimization, and operationalizing AI solutions in real-time enterprise environments.
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Language
English
Fluent
Javanese
Intermediate
Work Experience
Sr. AI/ML Engineer at Bank of America
February 1, 2024 - PresentDesigned and deployed scalable LLM solutions on AWS SageMaker, incorporating prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG) using LangChain. Integrated Claude and LLaMA models for enterprise GenAI use cases such as document summarization, intelligent search, and guided workflows. Developed and exposed LLM inference endpoints using FastAPI, enabling seamless integration with internal applications. Applied quantization and knowledge distillation techniques to reduce model size and improve inference speed without compromising accuracy. Built and managed GraphQL APIs with AWS API Gateway, enabling controlled, efficient access to GenAI services. Worked closely with product teams to align GenAI capabilities with business workflows, ensuring tangible outcomes and stakeholder buy-in. Implemented robust model versioning and A/B testing strategies using TorchServe and CloudFormation templates for controlled deployments. Processed real-time and batch data using AWS Glue,
AI/ ML Engineer at Microsoft
January 31, 2024 - July 11, 2025Designed and deployed serverless ML inference logic using Azure Functions, decreasing infrastructure overhead and improving response latency. Conducted model training, tuning, and tracking in Azure ML Studio, enabling reproducible experiments and streamlined deployment workflows. Developed Retrieval-Augmented Generation (RAG) pipelines with LangChain to enhance LLM outputs using enterprise knowledge bases. Fine-tuned transformer models using Hugging Face Transformers for custom NLP applications, including summarization and Q&A. Leveraged FAISS to enable fast similarity search and dense vector indexing in local development environments. Utilized Azure Databricks to handle large-scale data preprocessing and model fine-tuning on distributed compute resources. Managed structured and unstructured data in Azure Data Lake, supporting secure storage and access for training pipelines. Built RESTful APIs to expose LLM capabilities to downstream applications, enabling seamless integration with cl
ML Engineer at Mayo Clinic
April 30, 2022 - July 11, 2025Built and deployed ML models on Google Cloud Platform (GCP) using services like AI Platform, AutoML, and Vertex AI for scalable model training and deployment. Designed image and NLP solutions using Vision AI and Natural Language AI to automate document parsing and visual recognition workflows. Used Cloud Functions and Compute Engine to implement serverless and scalable infrastructure for event-driven ML tasks. Developed supervised and unsupervised learning models using Python, Pandas, NumPy, and Scikit-learn for classification, clustering, and regression problems. Leveraged PyTorch to build and train deep learning models, optimizing architectures for computer vision and natural language tasks. Performed model evaluation, cross-validation, and hyperparameter tuning to improve predictive accuracy and generalization on unseen data. Processed large-scale batch and streaming data using Google Cloud Dataflow and Apache Beam, supporting training and real-time inference use cases. Queried and
Data Scientist at BNSF Railway
November 30, 2019 - July 11, 2025Designed and maintained cloud-based data pipelines leveraging AWS S3 for storage, EC2 for compute, and RDS for relational database operations. Queried structured datasets using SQL, T-SQL, and PostgreSQL to extract insights and support data-driven decision-making. Developed and managed ETL workflows using Informatica for data ingestion, transformation, and loading across business domains. Orchestrated data workflows and ML pipelines using Apache Airflow to ensure reliable, scheduled execution of critical tasks. Built predictive models using Python and algorithms such as Random Forest, Logistic Regression, KNN, and Decision Trees for classification and forecasting. Applied feature engineering techniques to enhance model accuracy and improve downstream performance in the ML lifecycle. Trained and fine-tuned models on AWS SageMaker, optimizing hyperparameters for scalable production deployment. Created interactive KPI dashboards in Tableau to communicate insights, model performance, and b
Jr. Data Scientist at Hexaware
October 31, 2014 - July 11, 2025Performed data cleaning, transformation, and validation to ensure accuracy and consistency before model training and reporting activities. Conducted exploratory data analysis (EDA) using Python, Pandas, and NumPy to uncover trends, identify anomalies, and inform predictive modeling. Built Excel-based dashboards to present actionable insights to stakeholders without a dedicated BI platform. Utilized Scikit-learn for fundamental machine learning tasks, including feature selection and model evaluation for early-stage prototypes. Designed and maintained data flow diagrams and process maps using Microsoft Visio to document pipeline architecture and reporting workflows. Gathered product and competitor pricing data by implementing automated web scraping scripts to support market analysis and strategic planning. Collaborated with cross-functional teams in Agile ceremonies, such as sprint planning, daily stand-ups, and retrospectives, to align with project goals. Defined and monitored KPIs alig
Sr. AI/ML Engineer at Bank of America
February 1, 2024 - PresentDesigned and deployed scalable LLM solutions on AWS SageMaker, utilizing prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG). Integrated Claude and LLaMA models for enterprise GenAI applications including document summarization and intelligent search. Developed LLM inference endpoints using FastAPI and built GraphQL APIs with AWS API Gateway. Optimized models with quantization and knowledge distillation. Built ETL pipelines and automated CI/CD workflows using SageMaker Pipelines and AWS services. Led initiatives involving Docker and Kubernetes with strong security practices. Integrated DALL·E for multimodal applications and applied Responsible AI principles for bias mitigation and transparency. Mentored junior engineers while collaborating closely with product and engineering teams using Agile methodologies.
AI/ ML Engineer at Microsoft
January 31, 2024 - July 11, 2025Designed and deployed serverless ML inference using Azure Functions to improve latency and reduce overhead. Conducted model training, tuning, and tracking in Azure ML Studio. Developed RAG pipelines to enhance LLM outputs with LangChain. Fine-tuned transformers for NLP tasks using Hugging Face. Utilized FAISS for fast similarity search and Azure Databricks for large-scale data processing. Managed data in Azure Data Lake. Built RESTful APIs and lightweight GenAI backends with Flask. Converted models to ONNX for portability and optimized inference with TensorRT. Integrated Azure OpenAI models for conversational AI. Deployed containerized models on AKS and established CI/CD pipelines via Azure DevOps. Implemented responsible AI practices and monitored deployed endpoints for SLA adherence. Collaborated cross-functionally using Agile methodologies.
ML Engineer at Mayo Clinic
April 30, 2022 - July 11, 2025Built and deployed ML models on Google Cloud Platform services including AI Platform, AutoML, and Vertex AI. Developed computer vision and NLP solutions for document parsing and recognition. Used Cloud Functions and Compute Engine for scalable ML tasks. Developed supervised and unsupervised models using Python and PyTorch, performing extensive tuning and evaluation. Processed large-scale batch and streaming data using Dataflow and Apache Beam. Leveraged BigQuery for data querying and monitoring. Implemented CI/CD workflows using Jenkins and infrastructure provisioning with Terraform. Monitored model performance and collaborated closely with cross-functional teams using JIRA and Confluence. Developed Python-based data pipelines for training and evaluation workflows.
Data Scientist at BNSF Railway
November 30, 2019 - July 11, 2025Designed and maintained data pipelines leveraging AWS cloud services including S3, EC2, and RDS. Queried datasets using SQL and PostgreSQL. Developed ETL workflows with Informatica and orchestrated pipelines using Apache Airflow. Built predictive models with Python using algorithms such as Random Forest and Logistic Regression. Applied feature engineering and hyperparameter tuning. Trained models on AWS SageMaker for production use. Created interactive dashboards in Tableau to communicate insights. Conducted statistical analysis and participated in Agile Scrum ceremonies. Managed version control with Git and Bitbucket to support collaborative development.
Jr. Data Scientist at Hexaware
October 31, 2014 - July 11, 2025Performed data cleaning, transformation, and validation to ensure quality prior to modeling. Conducted exploratory data analysis using Python, Pandas, and NumPy. Built Excel dashboards for stakeholder insights. Used Scikit-learn for feature selection and model evaluation on early prototypes. Documented data flow diagrams using Microsoft Visio. Implemented automated web scraping for competitor analysis. Collaborated in Agile teams with sprint planning and retrospectives. Defined KPIs and visualized key metrics with Excel dashboards. Managed version control using Git, ensuring team code consistency.
Sr. AI/ML Engineer at Bank of America
February 1, 2024 - PresentDesigned and deployed scalable LLM solutions on AWS SageMaker, incorporating prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG) using LangChain. Integrated Claude and LLaMA models for enterprise GenAI use cases such as document summarization, intelligent search, and guided workflows. Developed and exposed LLM inference endpoints using FastAPI, enabling seamless integration with internal applications. Applied quantization and knowledge distillation techniques to reduce model size and improve inference speed without compromising accuracy. Built and managed GraphQL APIs with AWS API Gateway, enabling controlled, efficient access to GenAI services. Worked closely with product teams to align GenAI capabilities with business workflows, ensuring tangible outcomes and stakeholder buy-in. Implemented robust model versioning and A/B testing strategies using TorchServe and CloudFormation templates for controlled deployments. Processed real-time and batch data using AWS Glue,
AI/ ML Engineer at Microsoft
January 31, 2024 - July 11, 2025Designed and deployed serverless ML inference logic using Azure Functions, decreasing infrastructure overhead and improving response latency. Conducted model training, tuning, and tracking in Azure ML Studio, enabling reproducible experiments and streamlined deployment workflows. Developed Retrieval-Augmented Generation (RAG) pipelines with LangChain to enhance LLM outputs using enterprise knowledge bases. Fine-tuned transformer models using Hugging Face Transformers for custom NLP applications, including summarization and Q&A. Leveraged FAISS to enable fast similarity search and dense vector indexing in local development environments. Utilized Azure Databricks to handle large-scale data preprocessing and model fine-tuning on distributed compute resources. Managed structured and unstructured data in Azure Data Lake, supporting secure storage and access for training pipelines. Built RESTful APIs to expose LLM capabilities to downstream applications, enabling seamless integration with cl
ML Engineer at Mayo Clinic
April 30, 2022 - July 11, 2025Built and deployed ML models on Google Cloud Platform (GCP) using services like AI Platform, AutoML, and Vertex AI for scalable model training and deployment. Designed image and NLP solutions using Vision AI and Natural Language AI to automate document parsing and visual recognition workflows. Used Cloud Functions and Compute Engine to implement serverless and scalable infrastructure for event-driven ML tasks. Developed supervised and unsupervised learning models using Python, Pandas, NumPy, and Scikit-learn for classification, clustering, and regression problems. Leveraged PyTorch to build and train deep learning models, optimizing architectures for computer vision and natural language tasks. Performed model evaluation, cross-validation, and hyperparameter tuning to improve predictive accuracy and generalization on unseen data. Processed large-scale batch and streaming data using Google Cloud Dataflow and Apache Beam, supporting training and real-time inference use cases. Queried and
Data Scientist at BNSF Railway
November 30, 2019 - July 11, 2025Designed and maintained cloud-based data pipelines leveraging AWS S3 for storage, EC2 for compute, and RDS for relational database operations. Queried structured datasets using SQL, T-SQL, and PostgreSQL to extract insights and support data-driven decision-making. Developed and managed ETL workflows using Informatica for data ingestion, transformation, and loading across business domains. Orchestrated data workflows and ML pipelines using Apache Airflow to ensure reliable, scheduled execution of critical tasks. Built predictive models using Python and algorithms such as Random Forest, Logistic Regression, KNN, and Decision Trees for classification and forecasting. Applied feature engineering techniques to enhance model accuracy and improve downstream performance in the ML lifecycle. Trained and fine-tuned models on AWS SageMaker, optimizing hyperparameters for scalable production deployment. Created interactive KPI dashboards in Tableau to communicate insights, model performance, and b
Jr. Data Scientist at Hexaware
October 31, 2014 - July 11, 2025Data cleaning, transformation, and validation were performed to ensure accuracy and consistency before model training and reporting activities. Conducted exploratory data analysis (EDA) using Python, Pandas, and NumPy to uncover trends, identify anomalies, and inform predictive modeling. Built Excel-based dashboards to present actionable insights to stakeholders without a dedicated BI platform. Utilized Scikit-learn for fundamental machine learning tasks, including feature selection and model evaluation for early-stage prototypes. Designed and maintained data flow diagrams and process maps using Microsoft Visio to document pipeline architecture and reporting workflows. Gathered product and competitor pricing data by implementing automated web scraping scripts to support market analysis and strategic planning. Collaborated with cross-functional teams in Agile ceremonies, such as sprint planning, daily stand-ups, and retrospectives, to align with project goals. Defined and monitored KPIs
Education
Qualifications
Industry Experience
Financial Services, Software & Internet, Healthcare, Transportation & Logistics, Professional Services
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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