Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am Harika Beesetti, an AI/ML engineer specializing in Generative AI, LLM copilots, and Retrieval-Augmented Generation (RAG) pipelines. With 4+ years of experience building scalable ML systems across telecom and enterprise domains, I focus on delivering measurable business impact through automation and enterprise AI modernization. I enjoy collaborating with product and architecture teams to standardize LLMOps, governance, and secure cloud-native deployments (Azure, AWS, GCP). My passion lies in crafting reliable, responsible AI solutions that balance performance, observability, and ethical considerations while enabling rapid experimentation and deployment.…I am Harika Beesetti, an AI/ML engineer specializing in Generative AI, LLM copilots, and Retrieval-Augmented Generation (RAG) pipelines. With 4+ years of experience building scalable ML systems across telecom and enterprise domains, I focus on delivering measurable business impact through automation and enterprise AI modernization. I enjoy collaborating with product and architecture teams to standardize LLMOps, governance, and secure cloud-native deployments (Azure, AWS, GCP). My passion lies in crafting reliable, responsible AI solutions that balance performance, observability, and ethical considerations while enabling rapid experimentation and deployment.

Harika Beesetti

AI Engineer, Data Scientist, Full Stack Developer, +4





I am Harika Beesetti, an AI/ML engineer specializing in Generative AI, LLM copilots, and Retrieval-Augmented Generation (RAG) pipelines. With 4+ years of experience building scalable ML systems across telecom and enterprise domains, I focus on delivering measurable business impact through automation and enterprise AI modernization. I enjoy collaborating with product and architecture teams to standardize LLMOps, governance, and secure cloud-native deployments (Azure, AWS, GCP). My passion lies in crafting reliable, responsible AI solutions that balance performance, observability, and ethical considerations while enabling rapid experimentation and deployment.…I am Harika Beesetti, an AI/ML engineer specializing in Generative AI, LLM copilots, and Retrieval-Augmented Generation (RAG) pipelines. With 4+ years of experience building scalable ML systems across telecom and enterprise domains, I focus on delivering measurable business impact through automation and enterprise AI modernization. I enjoy collaborating with product and architecture teams to standardize LLMOps, governance, and secure cloud-native deployments (Azure, AWS, GCP). My passion lies in crafting reliable, responsible AI solutions that balance performance, observability, and ethical considerations while enabling rapid experimentation and deployment.

Available to hire

I am Harika Beesetti, an AI/ML engineer specializing in Generative AI, LLM copilots, and Retrieval-Augmented Generation (RAG) pipelines. With 4+ years of experience building scalable ML systems across telecom and enterprise domains, I focus on delivering measurable business impact through automation and enterprise AI modernization.

I enjoy collaborating with product and architecture teams to standardize LLMOps, governance, and secure cloud-native deployments (Azure, AWS, GCP). My passion lies in crafting reliable, responsible AI solutions that balance performance, observability, and ethical considerations while enabling rapid experimentation and deployment.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Gen AI Engineer at AT&T

September 1, 2024 - Present

Designed, developed, and deployed Generative AI solutions leveraging LLMs, diffusion models, and multimodal architectures for enterprise use cases. Fine-tuned foundation models via transfer learning and RLHF to improve accuracy and domain relevance. Built end-to-end ML pipelines for data ingestion, preprocessing, training, evaluation, and deployment using Python, TensorFlow, PyTorch, and Hugging Face Transformers. Engineered prompt templates, chains, and guardrails to ensure consistent and safe model responses; implemented workflows using LangChain and Copilot Studio. Deployed scalable AI applications on Azure (Azure ML, Databricks, Cognitive Services) and AWS (SageMaker, Lambda, EC2, S3) with containerized microservices and serverless functions. Developed MLOps practices with CI/CD pipelines, model versioning, automated retraining, and real-time monitoring for production-grade reliability. Integrated Generative AI models into enterprise applications, collaborating with full-stack team

AI Engineer at Tech Mahindra

July 1, 2024 - October 3, 2025

Designed and implemented end-to-end AI/ML solutions for predictive analytics, NLP, and computer vision using Python, TensorFlow, and PyTorch. Developed and fine-tuned LLMs and Generative AI applications for chatbots, copilots, and content automation. Built scalable machine learning pipelines including data ingestion, preprocessing, model training, deployment, and monitoring. Deployed AI models on AWS (SageMaker, Lambda, EC2) and Azure (ML, Databricks) using containerized microservices and serverless architectures. Applied MLOps best practices with CI/CD pipelines, model versioning, and automated retraining for production-grade reliability. Integrated structured data storage and optimized pipelines using PostgreSQL for model training and real-time inference. Collaborated with product, cloud, and full-stack teams to embed AI models into enterprise applications, enhancing automation and decision-making. Researched emerging AI frameworks and libraries to ensure adoption of best practices f

Full Stack Developer at Accenture

July 1, 2023 - October 3, 2025

Developed and maintained end-to-end enterprise applications as a Full Stack Developer, using Java, Spring Boot, and REST APIs for robust backend services. Designed scalable microservices architectures with Docker and Spring Cloud to ensure high availability and modularity. Built responsive front-end interfaces with React and Angular, integrated with Java backends. Optimized database performance with PostgreSQL, designing efficient queries and indexing. Deployed applications to AWS (EC2, S3, Lambda) and Azure App Services with CI/CD pipelines (Jenkins, GitHub Actions, or Azure DevOps). Implemented security best practices including OAuth2, JWT, and RBAC. Automated testing and code quality checks with JUnit, Mockito, and SonarQube to ensure reliability in production. Collaborated with cross-functional teams to deliver high-performance, user-centric web applications on schedule.

AI/ML- Gen AI Engineer at AT&T, USA

September 1, 2024 - Present

Designed, developed, and deployed Generative AI solutions using LLMs, diffusion models, and multimodal architectures for enterprise use cases. Fine-tuned foundation models using transfer learning and RLHF to improve accuracy and domain relevance. Built end-to-end AI/ML pipelines for data ingestion, preprocessing, training, evaluation, and deployment using Python, TensorFlow, PyTorch, and Hugging Face Transformers. Engineered prompt templates, chains, and guardrails; implemented workflows using LangChain and Copilot Studio. Deployed scalable AI applications on Azure and AWS with containerized microservices and serverless functions. Developed MLOps practices with CI/CD pipelines, model versioning, automated retraining, and real-time monitoring for production-grade reliability. Integrated Generative AI models into enterprise applications, collaborating with full-stack teams to deliver AI-driven copilots, chatbots, and recommendation engines. Implemented structured data management using Po

AI ENGINEER at Tech Mahindra, USA

July 31, 2024 - October 3, 2025

Designed and implemented end-to-end AI/ML solutions for predictive analytics, NLP, and computer vision. Developed and fine-tuned LLMs and Generative AI applications for chatbots, copilots, and content automation. Built scalable machine learning pipelines including data ingestion, preprocessing, model training, deployment, and monitoring. Deployed AI models on AWS and Azure using containerized microservices and serverless architectures. Applied MLOps practices with CI/CD pipelines, model versioning, and automated retraining for production-grade reliability. Collaborated with product, cloud, and full-stack teams to embed AI models into enterprise applications, enhancing automation and decision-making. Researched emerging AI frameworks and libraries to ensure adoption of best practices for performance, security, and cost efficiency.

Full Stack Developer at Accenture, India

July 31, 2023 - October 3, 2025

Developed and maintained end-to-end enterprise applications using Java, Spring Boot, and REST APIs for robust backend services. Designed scalable microservices architectures, leveraging Spring Cloud and Docker for high availability and modularity. Built responsive front-end interfaces integrated with Java backends. Optimized database performance with PostgreSQL and implemented security best practices (OAuth2, JWT, RBAC). Deployed applications to AWS and Azure with CI/CD pipelines and automated testing. Automated testing and code quality checks to ensure reliability in production. Collaborated with cross-functional teams to deliver high-performance, user-centric web applications on schedule.

AI/ML- Gen AI Engineer at AT&T, USA

September 1, 2024 - Present

Designed and implemented Generative AI copilots and Retrieval-Augmented Generation (RAG) systems using GPT-4, LangChain, and LlamaIndex on Azure ML and Cognitive Services. Integrated vector databases (FAISS, Pinecone, Azure AI Search) for low-latency, context-aware semantic retrieval. Enhanced embedding generation pipelines using Sentence Transformers, OpenAI, Cohere, and Instructor models to improve vector quality. Built end-to-end LLMOps pipelines in Azure Databricks and MLflow for large-scale fine-tuning (transfer learning, RLHF), automated deployment, and versioning via Azure DevOps, AKS, and GitHub Actions. Developed containerized deployments with Docker and Kubernetes (AKS), enabling CI/CD-driven model rollout. Automated GenAI performance benchmarking with LangSmith, RAGAS, and PromptLayer. Implemented model monitoring and drift detection with Evidently AI and Azure Monitor; extended observability with Prometheus and Grafana. Built multi-agent LangGraph workflows with AutoGen and

AI ENGINEER at Tech Mahindra, USA

July 1, 2024 - October 23, 2025

Built and deployed ML/DL models for NLP, recommendation, and time-series forecasting using PyTorch, TensorFlow, and scikit-learn. Fine-tuned Transformer architectures (BERT, RoBERTa, GPT-2/3, T5) with Hugging Face for classification, summarization, and QA. Created end-to-end NLP pipelines (preprocessing, tokenization, embeddings, training, inference) and scalable ETL/data pipelines (Pandas, NumPy, Apache Spark, Airflow). Applied hyperparameter optimization (Optuna, Ray Tune) and managed ML lifecycle with MLflow, Weights & Biases, and DVC. Deployed REST APIs (Flask, FastAPI) in Docker containers and orchestrated on AWS SageMaker and GCP Vertex AI. Implemented inference optimization and monitoring (ONNX, Prometheus, Grafana). Built LLM-powered retrieval and document Q&A apps using OpenAI GPT-3, LangChain, and FAISS; collaborated with cross-functional teams to align AI with business goals.

DEEP LEARNING ENGINEER at ACCENTURE, INDIA

July 1, 2023 - October 23, 2025

Developed and deployed deep learning models for NLP, time-series, and structured data tasks with PyTorch, TensorFlow, and Keras. Fine-tuned Transformer architectures for text classification, summarization, and NER; implemented scalable data/training pipelines (Airflow, Dask, Spark). Applied transfer learning and embedding techniques (Word2Vec, GloVe, FastText) with domain adaptation. Managed full ML lifecycle with MLflow, Weights & Biases, and DVC; deployed models via Flask/FastAPI and TensorFlow Serving on AWS SageMaker and GCP AI Platform; containerized with Docker and Kubernetes. Enhanced interpretability using SHAP, LIME, and Integrated Gradients. Collaborated in agile teams to deliver production-ready AI solutions while ensuring reproducibility and ethical AI practices.

MACHINE LEARNING RESEARCH INTERN at ANDHRA UNIVERSITY, INDIA

May 1, 2021 - October 23, 2025

Researched predictive analytics and classification models using Python, Scikit-learn, and Pandas for academic performance analysis. Applied Linear Regression, Decision Trees, and SVM with cross-validation to identify key patterns. Performed feature engineering and data preprocessing to improve dataset quality and model reliability. Visualized results with Matplotlib and documented findings; explored hyperparameter tuning to optimize performance and reduce overfitting.