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I am a Gen AI ML Full Stack Engineer with 5+ years of experience designing, training, and deploying deep learning models for NLP, computer vision, and multimodal applications. I am proficient in Java (Spring Boot, Kafka, Spark) for building microservices and real-time data pipelines, and I integrate ML models into enterprise-grade applications. I have hands-on experience developing large-scale AI systems with expertise in transformer architectures, LLM fine-tuning, and generative models (GANs, diffusion models, and reinforcement learning with human feedback). I have a strong Python background with TensorFlow and PyTorch, and I work with cloud platforms (AWS, GCP, Azure) to deliver end-to-end ML pipelines from data preprocessing to production deployment. I enjoy turning cutting-edge research into real-world solutions that improve user experiences and drive business impact. I value cross-functional collaboration, model performance optimization, and mentoring teams in scalable AI development best practices.…I am a Gen AI ML Full Stack Engineer with 5+ years of experience designing, training, and deploying deep learning models for NLP, computer vision, and multimodal applications. I am proficient in Java (Spring Boot, Kafka, Spark) for building microservices and real-time data pipelines, and I integrate ML models into enterprise-grade applications. I have hands-on experience developing large-scale AI systems with expertise in transformer architectures, LLM fine-tuning, and generative models (GANs, diffusion models, and reinforcement learning with human feedback). I have a strong Python background with TensorFlow and PyTorch, and I work with cloud platforms (AWS, GCP, Azure) to deliver end-to-end ML pipelines from data preprocessing to production deployment. I enjoy turning cutting-edge research into real-world solutions that improve user experiences and drive business impact. I value cross-functional collaboration, model performance optimization, and mentoring teams in scalable AI development best practices.

Padhu Padmini Bolem

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

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I am a Gen AI ML Full Stack Engineer with 5+ years of experience designing, training, and deploying deep learning models for NLP, computer vision, and multimodal applications. I am proficient in Java (Spring Boot, Kafka, Spark) for building microservices and real-time data pipelines, and I integrate ML models into enterprise-grade applications. I have hands-on experience developing large-scale AI systems with expertise in transformer architectures, LLM fine-tuning, and generative models (GANs, diffusion models, and reinforcement learning with human feedback). I have a strong Python background with TensorFlow and PyTorch, and I work with cloud platforms (AWS, GCP, Azure) to deliver end-to-end ML pipelines from data preprocessing to production deployment. I enjoy turning cutting-edge research into real-world solutions that improve user experiences and drive business impact. I value cross-functional collaboration, model performance optimization, and mentoring teams in scalable AI development best practices.…I am a Gen AI ML Full Stack Engineer with 5+ years of experience designing, training, and deploying deep learning models for NLP, computer vision, and multimodal applications. I am proficient in Java (Spring Boot, Kafka, Spark) for building microservices and real-time data pipelines, and I integrate ML models into enterprise-grade applications. I have hands-on experience developing large-scale AI systems with expertise in transformer architectures, LLM fine-tuning, and generative models (GANs, diffusion models, and reinforcement learning with human feedback). I have a strong Python background with TensorFlow and PyTorch, and I work with cloud platforms (AWS, GCP, Azure) to deliver end-to-end ML pipelines from data preprocessing to production deployment. I enjoy turning cutting-edge research into real-world solutions that improve user experiences and drive business impact. I value cross-functional collaboration, model performance optimization, and mentoring teams in scalable AI development best practices.

Available to hire

I am a Gen AI ML Full Stack Engineer with 5+ years of experience designing, training, and deploying deep learning models for NLP, computer vision, and multimodal applications. I am proficient in Java (Spring Boot, Kafka, Spark) for building microservices and real-time data pipelines, and I integrate ML models into enterprise-grade applications. I have hands-on experience developing large-scale AI systems with expertise in transformer architectures, LLM fine-tuning, and generative models (GANs, diffusion models, and reinforcement learning with human feedback).

I have a strong Python background with TensorFlow and PyTorch, and I work with cloud platforms (AWS, GCP, Azure) to deliver end-to-end ML pipelines from data preprocessing to production deployment. I enjoy turning cutting-edge research into real-world solutions that improve user experiences and drive business impact. I value cross-functional collaboration, model performance optimization, and mentoring teams in scalable AI development best practices.

Skills

Experience Level

Expert

Expert

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Expert

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Expert

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Expert

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Intermediate

Language

English

Fluent

Work Experience

Gen AI ML Full Stack Engineer at US Bank

March 1, 2024 - November 5, 2025

Led end-to-end ML pipeline development: from data ingestion and feature engineering to training, evaluation, and deployment. Built and deployed ML inference services in Java (Spring Boot) with REST APIs; integrated models into enterprise apps. Implemented real-time data pipelines using Kafka and Spark for streaming analytics and AI-driven decision systems. Engineered NLP and recommender analyses via Java microservices, ensuring low latency and scalability. Designed and deployed large-scale LLMs and generative models (GPT, diffusion, transformers, GANs) for NLP, vision, and multimodal tasks. Built RAG systems using vector databases to improve factual accuracy and reduce hallucinations. Implemented memory-augmented AI with LangChain for production-ready workflows. Fine-tuned pre-trained LLMs (LoRA, PEFT, RLHF) and integrated prompts. Led end-to-end pipelines with data ingestion, feature engineering, model training, evaluation, and cloud deployment. Optimized inference speed and training

AI ML Full Stack Engineer at Cognizant Technology Solutions

November 30, 2023 - November 30, 2023

Built and deployed AI-powered microservices in Java (Spring Boot) for real-time NLP inference and recommendations; implemented streaming data pipelines using Kafka and Spark. Used Java ML frameworks for training and serving deep learning models in enterprise apps. Engineered end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, and deployment. Retrieved augmented generation (RAG) systems by combining LLMs with vector stores; integrated LangChain and memory to enable multi-agent workflows. Finetuned LLMs with LoRA/PEFT/RLHF; built chatbots and knowledge assistants for customer support and enterprise search. Deployed production ML services on cloud platforms (AWS, GCP, Azure) using Docker and Kubernetes; implemented A/B testing and KPI-based evaluation. Drove model efficiency via quantization, pruning, and distributed training; improved latency and throughput. Collaborated with product and data engineering teams to translate research into production-re

Data Scientist / ML Engineer at Cognizant Technology Solutions

November 30, 2022 - November 30, 2022

Built Java-based microservices to deploy and scale AI/ML models in production environments. Implemented real-time data processing pipelines in Java using Apache Kafka and Spark to support AI-driven applications. Designed end-to-end ML systems from raw data ingestion to deployment, ensuring scalability and reliability. Developed recommendation engines and NLP pipelines using embeddings and deep learning techniques. Created customization pipelines for feature engineering, model training, and evaluation; established monitoring and governance to maintain model performance in production. Implemented probabilistic inference and Bayesian methods for business problem solving; developed NLP solutions for summarization, semantic search, and topic modeling with HuggingFace, spaCy, and BERT-based models. Automated real-time data pipelines with Kafka, Spark, and Airflow; integrated feature stores and monitoring systems for online ML models. Leveraged cloud-native ML platforms (AWS SageMaker, GCP Ve

AI/ML Full Stack Engineer at Cognizant Technology Solutions

November 1, 2023 - November 1, 2023

Built and deployed AI-powered microservices in Java (Spring Boot) for real-time inference of NLP and recommendation models. Implemented data streaming pipelines in Java using Apache Kafka and Spark, enabling low-latency ML workflows. Utilized Java ML frameworks (Deeplearning4j, Tribuo) for training and serving deep learning models in enterprise applications. Engineered Retrieval-Augmented Generation (RAG) systems combining LLMs with vector databases (Pinecone) to enable domain-specific knowledge retrieval. Designed scalable AI applications using LangChain and LangGraph, enabling multi-agent workflows, dynamic reasoning, and tool integration for enterprise use cases.

AI/ML Full Stack Engineer at Cognizant Technology Solutions

January 1, 2024 - November 5, 2025

Built and deployed AI-powered microservices in Java (Spring Boot) for real-time NLP inference and recommendation models. Implemented data streaming pipelines using Apache Kafka and Spark, enabling low-latency ML workflows. Used Java ML frameworks (Deeplearning4j, Tribuo) for training and serving deep learning models in enterprise applications. Engineered Retrieval-Augmented Generation (RAG) systems with vector databases (Pinecone) to enable domain-specific knowledge retrieval. Designed scalable AI applications using LangChain and LangGraph, enabling multi-agent workflows, dynamic reasoning, and tool integration for enterprise use cases.

Data Scientist / ML Engineer at Cognizant Technology Solutions

November 1, 2022 - November 1, 2022

Developed Java-based microservices for deploying and scaling AI/ML models in production environments. Implemented real-time data processing pipelines in Java using Apache Kafka and Spark to support AI-driven applications. Designed end-to-end ML systems from raw data ingestion to deployment, ensuring scalability and reliability. Built recommendation engines, fraud detection, and network analysis via graph ML, NLP solutions for summarization, sentiment analysis, and topic modeling using spaCy and BERT-based models. Automated data ingestion pipelines with Kafka, Spark, Airflow; integrated feature stores and monitoring for online ML models; deployed with Kubernetes. Built offline/online NLP solutions for summarization, entity extraction, and sentiment classification; designed and automated data pipelines, integrated with memory stores, and monitoring.

Gen AI ML Full Stack Engineer at US Bank

March 1, 2024 - November 11, 2025

Developed and deployed ML inference services in Java using Spring Boot and REST APIs; implemented real-time data pipelines with Kafka and Spark; built Java-based microservices for NLP models and recommendation engines; designed and deployed large-scale LLMs and generative models (GPT, Diffusion, Transformers, GANs) for NLP, vision, and multimodal tasks; built Retrieval-Augmented Generation (RAG) systems using vector databases to enhance LLM factual accuracy and reduce hallucinations; created end-to-end ML/GenAI pipelines covering data ingestion to production deployment; optimized inference speed and training costs via quantization, pruning, batching, and distributed training on GPUs/TPUs; delivered production-grade ML APIs and microservices using FastAPI, Docker, Kubernetes, and cloud platforms (Azure ML); conducted A/B testing and evaluation frameworks to measure business impact; collaborated with cross-functional teams to integrate GenAI into chatbots, recommendation engines, knowled

AI ML Full Stack Engineer at Cognizant Technology Solutions

November 1, 2023 - November 1, 2023

Built AI-powered microservices in Java (Spring Boot) for real-time inference of NLP and recommendation models; implemented data streaming and preprocessing pipelines using Kafka and Spark; utilized JVM-based ML frameworks for training and serving deep learning models; engineered and deployed retrieval-augmented generation (RAG) systems using vector databases to enhance LLM factual accuracy; designed scalable AI applications using LangChain and LangGraph; Spark-based LLM fine-tuning and optimization; end-to-end ML pipelines with automation; deployed production-grade ML services with FastAPI, Docker, Kubernetes, and cloud platforms; improved model efficiency through quantization, pruning, and distillation; delivered AI-powered products such as intelligent chatbots; built evaluation frameworks combining offline metrics and online A/B testing; collaborated with cross-functional teams to translate research into enterprise-ready AI solutions; integrated MLOps practices.

Data Scientist/ ML Engineer at Cognizant Technology Solutions

November 30, 2022 - November 30, 2022

Developed Java-based microservices for deploying and scaling AI/ML models in production environments. Implemented real-time data processing pipelines in Java using Apache Kafka and Spark to support AI-driven applications. Used Java ML frameworks (DeepLearning4J, Tribuo) for training and serving deep learning models in enterprise applications. Engineered recommendation engines and personalization pipelines using embeddings, collaborative filtering, and deep learning techniques. Built time-series forecasting models to optimize demand planning and resource allocation, improving forecast accuracy by 20–30%. Implemented graph machine learning for fraud detection, social network analysis, and knowledge graph-based search. Created custom NLP solutions for summarization, semantic search, intent classification, and topic modeling using HuggingFace, spaCy, and BERT-based models. Implemented end-to-end data pipelines with Kafka, Spark, and Airflow, reducing latency for ML workflows. Integrated

Gen AI ML Full Stack Engineer

January 1, 2024 - November 24, 2025

Led development and deployment of ML model inference services in Java using Spring Boot and REST APIs, integrating AI models into enterprise apps. Implemented real-time data pipelines in Java with Kafka and Spark, building microservices to serve NLP and recommender models with low latency and high scalability. Designed and deployed large-scale LLMs and generative models (GPT, diffusion, transformers, GANs) for NLP, vision, and multimodal tasks. Built Retrieval-Augmented Generation (RAG) systems using vector databases to improve factual accuracy and reduce hallucinations. Delivered end-to-end ML pipelines—from data ingestion and feature engineering to model training, evaluation, and cloud deployment. Optimized inference speed and training costs through quantization, pruning, batching, and distributed training on GPUs/TPUs. Deployed production-grade ML APIs and microservices using FastAPI, Docker, Kubernetes, and cloud-native platforms (Azure ML). Conducted A/B testing to measure impac

AI ML Full Stack Engineer

November 1, 2023 - November 1, 2023

Built and deployed AI-powered microservices in Java (Spring Boot) for real-time NLP and recommender model inference. Implemented data streaming and preprocessing pipelines in Java using Apache Kafka and Spark. Leveraged Java ML frameworks (Deep Learning4J, Tribuo) for training and serving models in enterprise apps. Engineered Retrieval-Augmented Generation (RAG) systems combining LLMs with vector databases to enable domain-specific knowledge retrieval. Designed scalable AI applications using LangChain and LangGraph, enabling multi-agent workflows, dynamic reasoning, and tool integration for enterprise use cases. Led LLM fine-tuning and optimization (LoRA, PEFT, RLHF, prompt engineering) to improve task-specific performance. Built end-to-end ML pipelines with data management, feature engineering, model training, evaluation, and cloud deployment. Deployed production-grade ML services with FastAPI, Docker, Kubernetes, and cloud platforms. Improved model efficiency via quantization, prunin

Data Scientist / ML Engineer

November 1, 2022 - November 1, 2022

Developed Java-based microservices for deploying and scaling AI/ML models in production environments. Implemented real-time data processing pipelines in Java using Kafka and Spark to support AI-driven applications. Designed end-to-end ML systems from raw data ingestion to deployment, ensuring scalability and reliability. Built recommendation engines and personalization pipelines using embeddings, collaborative filtering, and deep learning techniques. Implemented graph machine learning for fraud detection, social network analysis, and knowledge graph-based search. Created NLP solutions for summarization, semantic search, intent classification, and topic modeling using Hugging Face, spaCy, and BERT-based models. Automated real-time data pipelines with Kafka, Spark, and Airflow, reducing data latency. Integrated feature stores and monitoring systems for online ML models to ensure data consistency and drift detection. Leveraged cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure