AI Engineer with 6 years of experience building scalable AI, Generative AI, and machine learning solutions for enterprise environments. Experienced in developing AI copilots, intelligent search platforms, document understanding systems, recommendation engines, and MLOps pipelines. Strong expertise in Python, LangChain, LlamaIndex, PyTorch, Azure OpenAI, AWS, Kubernetes, and cloud-based AI deployments.
Expertise & skills
Selected projects
Procurement AI Copilot
Developed an AI copilot that assisted procurement teams by answering policy questions, summarizing supplier documents, and generating purchase recommendations.
- Built document ingestion pipelines.
- Implemented vector-based semantic search.
- Designed RAG workflows.
- Integrated enterprise APIs.
- Optimized prompts for higher response accuracy.
Energy Consumption Forecasting
Built forecasting models to predict energy demand for industrial facilities.
- Prepared large-scale datasets.
- Developed forecasting models.
- Automated model retraining.
- Improved prediction accuracy through feature engineering.
Contract Review Assistant
Created an AI assistant to analyze contracts, identify key clauses, and generate concise summaries.
- Developed document indexing pipelines.
- Implemented clause extraction.
- Integrated secure APIs.
- Enhanced summarization quality with prompt tuning.
Personalized Learning Recommendation Engine
Designed a recommendation system for an online learning platform to personalize course suggestions.
- Built recommendation algorithms.
- Engineered user behavior features.
- Optimized ranking models.
- Exposed recommendation APIs.
Core competencies
Certifications
- Microsoft Certified: Azure AI Engineer Associate
- AWS Certified AI Practitioner
- LangChain for LLM Application Development
- PyTorch Deep Learning Professional Certificate
- Machine Learning Engineering for Production (MLOps)
Education
Bachelor of Engineering (Computer Engineering)
Gujarat Technological University
Achievements
- Delivered 17+ enterprise AI initiatives.
- Reduced document review effort by 72% using AI-powered automation.
- Improved recommendation accuracy by 28%.
- Built reusable AI components used across multiple enterprise solutions.
- Successfully deployed production AI workloads using Kubernetes.
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