Hire Beesetti H. - Fine-Tuning Engineer

* ● 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 (Reinforcement Learning with Human Feedback) 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 to deliver consistent and safe model responses; implemented workflows using LangChain and Copilot Studio. * ● Deployed scalable AI applications on Azure Cloud (Azure ML, Databricks, Cognitive Services) and AWS (SageMaker, Lambda, EC2, S3) using 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 PostgreSQL for consistent storage, optimized queries, and seamless integration with AI workflows. * ● Researched and evaluated emerging Generative AI frameworks and tools, ensuring adoption of best practices for performance, security, and cost efficiency. * ● Collaborated with cross-functional stakeholders to translate business requirements into AI solutions, enhancing automation, decision-making, and customer experience.

* ● 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 (Large Language Models) 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 for performance, security, and cost efficiency.