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AI/ML engineer with 10+ years of software experience specializing in NLP and Generative AI/LLMs. Proven expertise in building, deploying, and fine-tuning models using PyTorch, TensorFlow, scikit-learn, and Hugging Face, along with RAG-based LLM applications. Hands-on in MLOps and cloud-native deployments across AWS/Azure/GCP, with strong CI/CD, Docker/Kubernetes, and monitoring practices. Experienced collaborating with cross-functional teams to deliver production-ready AI systems with measurable efficiency and customer impact.…AI/ML engineer with 10+ years of software experience specializing in NLP and Generative AI/LLMs. Proven expertise in building, deploying, and fine-tuning models using PyTorch, TensorFlow, scikit-learn, and Hugging Face, along with RAG-based LLM applications. Hands-on in MLOps and cloud-native deployments across AWS/Azure/GCP, with strong CI/CD, Docker/Kubernetes, and monitoring practices. Experienced collaborating with cross-functional teams to deliver production-ready AI systems with measurable efficiency and customer impact.

SAI BANOTH

AI Engineer, Data Scientist, Data Analyst, +3

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AI/ML engineer with 10+ years of software experience specializing in NLP and Generative AI/LLMs. Proven expertise in building, deploying, and fine-tuning models using PyTorch, TensorFlow, scikit-learn, and Hugging Face, along with RAG-based LLM applications. Hands-on in MLOps and cloud-native deployments across AWS/Azure/GCP, with strong CI/CD, Docker/Kubernetes, and monitoring practices. Experienced collaborating with cross-functional teams to deliver production-ready AI systems with measurable efficiency and customer impact.…AI/ML engineer with 10+ years of software experience specializing in NLP and Generative AI/LLMs. Proven expertise in building, deploying, and fine-tuning models using PyTorch, TensorFlow, scikit-learn, and Hugging Face, along with RAG-based LLM applications. Hands-on in MLOps and cloud-native deployments across AWS/Azure/GCP, with strong CI/CD, Docker/Kubernetes, and monitoring practices. Experienced collaborating with cross-functional teams to deliver production-ready AI systems with measurable efficiency and customer impact.

Available to hire

AI/ML engineer with 10+ years of software experience specializing in NLP and Generative AI/LLMs. Proven expertise in building, deploying, and fine-tuning models using PyTorch, TensorFlow, scikit-learn, and Hugging Face, along with RAG-based LLM applications.

Hands-on in MLOps and cloud-native deployments across AWS/Azure/GCP, with strong CI/CD, Docker/Kubernetes, and monitoring practices. Experienced collaborating with cross-functional teams to deliver production-ready AI systems with measurable efficiency and customer impact.

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Work Experience

Sr. Gen AI Engineer at Bank Of America

November 1, 2024 - Present

Designed, developed, and deployed AI/ML models including generative AI and LLM-based solutions. Built and optimized prompts for OpenAI GPT-3/4 use cases such as understanding, summarization, and content generation. Prototyped chatbots, writing assistants, and automation agents using GPT APIs and Python SDKs, and exposed outputs via REST APIs. Implemented fine-tuning with Hugging Face for domain-specific performance improvements and built RAG workflows for knowledge-intensive tasks. Developed data pipelines using Databricks and Delta Lake and integrated cloud deployments across AWS/Azure/GCP. Led implementation of CI/CD/CT pipelines using Docker, Kubernetes, and Jenkins; built supporting MLOps lifecycle practices for smoother experimentation-to-production transitions. Collaborated with cross-functional teams to align prompt/model behavior with business objectives and delivered enterprise integration using REST/GraphQL and modern frontend UI frameworks.

Sr. AI/ML Engineer at AT&T

January 1, 2023 - October 31, 2024

Architected cloud-native ML pipelines using SageMaker Pipelines, Argo Workflows, and Apache Airflow for scalable, reproducible training/validation/deployment. Automated model versioning, lineage tracking, and deployments with MLflow integrated with Kubernetes workloads. Built containerized inference environments using Docker; deployed serving endpoints using Triton Inference Server and TensorFlow Serving on Amazon EKS. Standardized infrastructure with Terraform, Helm, and Kustomize; managed secrets via Vault and implemented secure APIs using FastAPI with OAuth2 and AWS Secrets Manager. Implemented dataset/artifact governance with DVC and GitOps pipelines. Established CI/CD using GitHub Actions for container builds and automated endpoint rollouts. Added production monitoring via CloudWatch and operational explainability (SHAP/LIME). Implemented RAG with vector databases (FAISS/Pinecone) for real-time telecom event analysis and used LoRA fine-tuning for telecom operations such as anomaly

Data Scientist at Massachusetts General Hospital

January 1, 2020 - December 31, 2022

Partnered with clinical and operational teams to translate healthcare business problems into analytical and predictive solutions. Built PySpark/Spark SQL workflows to process patient/claims/operational data at scale. Developed predictive models (scikit-learn, XGBoost) for risk factors, readmission trends, and resource utilization patterns. Performed EDA with Python (Pandas/NumPy) and implemented feature engineering for missing values, normalization, and encoding. Designed reusable data preparation pipelines using Python and SQL. Built and optimized SQL queries (Teradata and relational sources) for extraction, transformation, and validation. Evaluated models using RMSE/MAE and classification metrics including precision/recall/F1 and ROC-AUC with cross-validation. Produced dashboards in Tableau for operational and leadership reporting and worked within Agile processes including sprint planning, code reviews, testing, and stakeholder demos.

Data Scientist at Ford Motors

May 1, 2018 - October 31, 2020

Analyzed manufacturing/production datasets to support operational and engineering decision-making. Built scalable data processing pipelines using Python/SQL/Apache Spark for manufacturing and sensor data. Conducted EDA to identify trends, anomalies, and opportunities for quality improvement. Implemented feature engineering pipelines with Pandas/NumPy and developed forecasting models for demand and inventory/supply chain performance. Built classification and regression models for defect detection and quality control. Validated model performance using cross-validation and metrics such as RMSE/MAE, precision/recall, and ROC-AUC. Developed Tableau dashboards for manufacturing KPIs and partnered with data engineers to optimize ETL workflows. Automated reporting tasks using Python scripts to reduce manual effort. Participated in Agile delivery including planning, reviews, and production support.

Sr. Data Analyst at Amazon

August 1, 2016 - April 30, 2018

Performed data analysis, migration, and preparation for segmentation, affinity analysis, and personalized marketing use cases. Built analytical workflows in Python (Pandas/NumPy/Seaborn/SciPy/Matplotlib) and applied ML/NLP tools (scikit-learn, NLTK) to analyze customer behavior and sales trends. Created data warehousing and ETL pipelines for extracting and transforming retail transaction, inventory, and customer datasets. Implemented algorithms for demand forecasting, churn prediction, and product recommendation, and added multivariate data validation scripts to improve data quality. Worked on ETL process optimization efforts and built reporting dashboards in Tableau with advanced calculated fields and dynamic filtering. Performed Git administration and supported data governance. Developed front-end UI web applications using jQuery/JavaScript/NodeJS for customer/order experiences and published Tableau reports to Tableau Server.

Data Analyst at Walmart

November 1, 2014 - July 31, 2016

Gathered requirements with stakeholders and supported data standardization through data modeling. Extracted historical and real-time datasets with Hadoop technologies (Hive/MapReduce/HDFS). Developed custom Hive UDFs for string/date/data manipulations. Cleaned data and performed feature scaling/engineering using Python. Applied clustering (K-Means, hierarchical clustering) for customer and product segmentation. Built Tableau dashboards and interactive reports with advanced calculated fields, parameters, sets, groups, actions, and statistical analysis. Published workbooks to Tableau Server with row-level security and automated refreshes for operational reporting.