Mohammad Afroz Shaik

About Mohammad Afroz Shaik

I’m an AI/ML Engineer with 5+ years of experience building, deploying, and scaling production-grade Machine Learning and Generative AI systems across cloud platforms and enterprise environments.My expertise spans Large Language Models (LLMs), MLOps, and distributed data pipelines, with a strong focus on translating complex business requirements into secure, scalable, and high-impact AI solutions. I’ve delivered AI-powered features for SaaS platforms, including chatbots, developer assistants, and enterprise knowledge retrieval systems, driving measurable improvements in efficiency, accuracy, and customer experience.Currently, I work on Generative AI and NLP solutions involving LLM fine-tuning, Retrieval-Augmented Generation (RAG), semantic search, and ML lifecycle automation, leveraging tools such as Hugging Face, OpenAI APIs, LangChain, Pinecone, MLflow, and Databricks Lakehouse.I bring strong experience in end-to-end ML systems, including data engineering, feature engineering, model training, CI/CD for ML, monitoring, and drift detection—deploying models at scale using Docker, Kubernetes, and cloud ML platforms (AWS, Azure, GCP).I enjoy collaborating with product managers, data engineers, and platform teams to build responsible, cost-efficient, and production-ready AI systems that deliver real business value.

Experience

1. Ai Ml Engineer

   Databricks

   Jan 2024 — Present

   Built Generative AI features for SaaS platforms, fine-tuning LLMs with Hugging Face Transformers, OpenAI APIs, and AION to power production chatbots, developer assistants, and self-service support, reducing ticket resolution time by 25%.• Deployed scalable ML and NLP services on the Databricks Lakehouse using Python, PySpark, Delta Lake, and MLflow, cutting training runtime while improving experiment tracking, reproducibility, and governance.• Implemented RAG pipelines using LangChain and Pinecone to enable semantic search across product documentation, APIs, runbooks, and engineering knowledge bases.• Implemented production-grade MLOps pipelines for AI-powered features using GitHub Actions, Docker, and Kubernetes, automating CI/CD and accelerating model release cycles from 2 weeks to 5 days across multiple engineering teams.• Collaborated with product managers, platform engineers, and data teams to translate roadmap requirements into secure, scalable, cost-efficient AI solutions, aligning with cloud architecture standards, data governance, and Responsible AI practices.

Education

• University of North Texas

  Master of Science, Computer Science