AI Engineer with 6 years of experience building enterprise AI platforms using Generative AI, Machine Learning, Deep Learning, NLP, and MLOps. Skilled in developing AI copilots, autonomous agents, intelligent search systems, document intelligence solutions, and predictive analytics applications with modern LLM frameworks and cloud technologies.
Expertise & skills
Selected projects
Smart City Operations Assistant
Developed an AI assistant that analyzed city operation reports, citizen complaints, and maintenance records to provide actionable insights.
- Built document ingestion and indexing pipelines.
- Implemented semantic search using vector embeddings.
- Designed multi-agent workflows.
- Integrated enterprise REST APIs.
- Optimized prompts for higher answer accuracy.
Predictive Equipment Maintenance
Created predictive maintenance models for industrial machinery using sensor data.
- Developed feature engineering pipelines.
- Built anomaly detection and forecasting models.
- Automated retraining workflows.
- Published scalable prediction APIs.
Research Paper Intelligence Platform
Built an AI platform for searching, summarizing, and comparing research papers across multiple domains.
- Implemented document indexing.
- Developed RAG pipelines.
- Created secure API services.
- Enhanced summarization with prompt engineering.
Digital Marketing Recommendation Engine
Developed an AI solution that recommended campaign optimizations based on historical marketing performance.
- Built recommendation models.
- Performed data preprocessing.
- Optimized ranking algorithms.
- Integrated inference APIs.
Core competencies
Certifications
- Microsoft Certified: Azure AI Engineer Associate
- AWS Certified Machine Learning – Specialty
- Generative AI with Large Language Models
- Advanced LangGraph for AI Agents
- TensorFlow Developer Certificate
Education
Bachelor of Engineering (Computer Science)
Gujarat Technological University
Achievements
- Delivered 21+ enterprise AI initiatives.
- Reduced document search time by 76% using RAG solutions.
- Improved predictive model accuracy by 29%.
- Built reusable AI microservices for cross-functional platforms.
- Successfully deployed cloud-native AI applications with Kubernetes.
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