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Generative AI / AI & Machine Learning Engineer with 5 years of experience building and deploying production-scale solutions across NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs). Hands-on in RAG, AI agents, conversational AI, and enterprise knowledge assistants. Experienced in MLOps and cloud-native architectures, including Docker, Kubernetes, AWS/Azure, CI/CD, prompt engineering, and scalable vector-search systems. Strong track record improving accuracy, reducing hallucinations, and increasing efficiency for business-critical workflows.…Generative AI / AI & Machine Learning Engineer with 5 years of experience building and deploying production-scale solutions across NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs). Hands-on in RAG, AI agents, conversational AI, and enterprise knowledge assistants. Experienced in MLOps and cloud-native architectures, including Docker, Kubernetes, AWS/Azure, CI/CD, prompt engineering, and scalable vector-search systems. Strong track record improving accuracy, reducing hallucinations, and increasing efficiency for business-critical workflows.

Adusumalli Manikanta

AI Engineer, Data Scientist, AI Developer, +3

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Generative AI / AI & Machine Learning Engineer with 5 years of experience building and deploying production-scale solutions across NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs). Hands-on in RAG, AI agents, conversational AI, and enterprise knowledge assistants. Experienced in MLOps and cloud-native architectures, including Docker, Kubernetes, AWS/Azure, CI/CD, prompt engineering, and scalable vector-search systems. Strong track record improving accuracy, reducing hallucinations, and increasing efficiency for business-critical workflows.…Generative AI / AI & Machine Learning Engineer with 5 years of experience building and deploying production-scale solutions across NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs). Hands-on in RAG, AI agents, conversational AI, and enterprise knowledge assistants. Experienced in MLOps and cloud-native architectures, including Docker, Kubernetes, AWS/Azure, CI/CD, prompt engineering, and scalable vector-search systems. Strong track record improving accuracy, reducing hallucinations, and increasing efficiency for business-critical workflows.

Available to hire

Generative AI / AI & Machine Learning Engineer with 5 years of experience building and deploying production-scale solutions across NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs). Hands-on in RAG, AI agents, conversational AI, and enterprise knowledge assistants.

Experienced in MLOps and cloud-native architectures, including Docker, Kubernetes, AWS/Azure, CI/CD, prompt engineering, and scalable vector-search systems. Strong track record improving accuracy, reducing hallucinations, and increasing efficiency for business-critical workflows.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Beginner

Beginner

Beginner

Language

English

Advanced

Telugu

Advanced

Hindi

Intermediate

Work Experience

AI/ML Engineer at Capital One

January 1, 2025 - Present

Designed machine learning solutions using Regression Models, Random Forest, and XGBoost to improve loan risk prediction accuracy by 21%, reduce false positives by 16%, and enhance credit decision workflows. Built LLM-powered applications using LLaMA and LlamaIndex, integrating enterprise and external data to increase response precision by 34% and reduce irrelevant outputs by 26%. Implemented RAG using LangChain/LangGraph and OpenAI GPT-4 to improve response accuracy by 38% and enable semantic search across large enterprise knowledge bases while reducing hallucinations. Developed multi-step LLM agents using LangGraph, CrewAI, and the OpenAI API for tool calling and workflow orchestration, reducing manual effort by 52% and improving business efficiency. Built scalable statistical and ML solutions processing 1.5M+ records to improve forecasting accuracy by 24%.

AI/ML Engineer at Goldman Sachs

December 1, 2021 - July 31, 2023

Developed and deployed generative AI and NLP solutions using transformer-based models, prompt engineering, embeddings, and RAG to automate financial document analysis, knowledge retrieval, and insight generation across enterprise workflows. Built scalable LLM/RAG pipelines for ingestion, preprocessing, chunking, embedding generation, and semantic search, reducing manual research effort by 32% and improving retrieval accuracy. Engineered production-grade inference services with Python, FastAPI, Docker, Kubernetes, and AWS to improve serving response times by 41% and support highly available enterprise AI applications. Designed end-to-end ML/MLOps pipelines using Apache Airflow and CI/CD workflows for automated ingestion, feature engineering, training, validation, and deployment, improving release efficiency and reducing manual activities. Developed ML models for risk analysis, anomaly detection, classification, and predictive analytics with Python/PyTorch/TensorFlow/Scikit-learn with sy

Jr. AI/ML Engineer at Kpit Technologies

August 1, 2019 - November 30, 2021

Optimized ANN models for classification and regression using feature engineering, hyperparameter tuning, and performance optimization in Python/TensorFlow/Keras to improve prediction accuracy by 35%. Deployed serverless applications on AWS Lambda, reducing processing time by 40% using event-driven automation with S3, API Gateway, CloudWatch, and DynamoDB. Created interactive analytical reports and visualization of model performance using Matplotlib to enable continuous monitoring of training metrics and accelerate performance analysis by 29%. Implemented sentiment analysis solutions for customer reviews to improve sentiment prediction accuracy by 27% and generate engagement insights. Built high-performance data preprocessing pipelines with NumPy and Pandas for cleansing and feature engineering across structured and unstructured datasets, improving preparation efficiency by 42% and reducing training time by 26%.