Looking for a job

Akshay Es

@akshayes

I build production-ready AI agents, RAG pipelines, and machine learning applications using Python and modern AI frameworks.

India

Message

What I'm looking for

I'm looking to build production-ready AI and machine learning applications, especially AI agents, RAG systems, computer vision, and data-driven products using modern Python frameworks.

I've built end-to-end machine learning and generative AI applications, from model development and evaluation to deployment with FastAPI, Docker, and Streamlit.

At Luminar Technolab, I developed machine learning pipelines across regression, classification, and clustering, achieving R = 0.96. I also built RAG and computer-vision workflows, including real-time exercise classification using pose estimation, and automated deployment processes that reduced manual processing time by 40%.

My recent projects include an AI Business Intelligence Analyst that safely generates, validates, and executes SQL from natural-language requests, and an AI-powered LinkedIn Post Generator using LangChain, Groq, few-shot prompting, and multilingual output.

Experience

Work history, roles, and key accomplishments

Data Science Intern

Luminar Technolab

Aug 2024 - Apr 2025 (8 months)

Developed and deployed machine learning pipelines using Python, TensorFlow, PyTorch, and Scikit-learn, achieving R = 0.96 across regression, classification, and clustering models. Built Generative AI applications using LangChain, LangGraph, LLMs, RAG, and vector databases, and automated AI model deployment workflows using Python, REST APIs, and Git.

Python SQL Power BI scikit learn TensorFlow PyTorch Lang Chain LangGraph LLM RAG Vector Databases Mediapipe fastAPI Git Model Evaluation

Data Science Intern

Luminar Technolab

Aug 2024 - Apr 2025 (8 months)

Developed and deployed machine learning pipelines using Python, TensorFlow, PyTorch, and Scikit-learn, achieving R = 0.96 across regression, classification, and clustering models. Built Generative AI applications using LangChain, LangGraph, LLMs, RAG, and vector databases, improving retrieval-based response accuracy across 2 production-ready AI workflows.