AI Engineer with 1+ years in LLM & ML Systems
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AI Engineer with 2 years of experience building production-grade LLM and ML systems, including RAG pipelines, AI agents, and domain-specific SLMs. Skilled in designing scalable AI workflows, synthetic data generation, and predictive analytics solutions such as demand forecasting and churn prediction. Experienced in developing end-to-end ML frameworks that enable automated model training, evaluation, and deployment. Focused on delivering high-impact, scalable AI products.
Loyola College, Chennai
B.Sc. · Computer Science
August 1, 2021 – June 30, 2024
Loyola Mat Hr Sec School, Chennai
HSC
N/A – Present
United Techno Info Systems
AI/ML Engineer
August 1, 2024 – Present
India
SLM Development
June 1, 2026 – Present
Built domain-specific SLMs for product classification and recommendations. Integrated TruLens for model evaluation (fairness & explainability).
QA Genie & QA Genie Pro
June 1, 2026 – Present
Led development of an AI-based test case generation system for QA automation reducing manual effort by ~70% (QA Genie Pro). Integrated into JIRA platform (QA Genie).
Synthetic Data Generator
June 1, 2026 – Present
Developed ML (SDV) and AI-based synthetic data generation system. Used for model training and testing in low-data scenarios.
AI-Powered Analytical Query Generation System
June 1, 2026 – Present
Built an NL-to-SQL system using LLMs to enable non-technical users to query structured data using natural language. Improved data accessibility for both technical and non-technical users.
AI Data Validation Framework
June 1, 2026 – Present
Designed validation system for both Data Engineers and ML Engineers. Automated anomaly detection and data quality checks.
AI Agentic Studio (AI Agents Framework)
June 1, 2026 – Present
Built a studio for creating and deploying AI agents for workflow automation and intelligent decision-making, reducing manual effort.
RAG-based Intelligent Query System
June 1, 2026 – Present
Implemented RAG pipeline with NER and schema linking. Enabled semantic search and context-aware query answering across structured and unstructured data sources. Integrated vector databases (FAISS / Pinecone).
ML Framework for Automated Pipelines
June 1, 2026 – Present
Built an end-to-end ML framework automating data preprocessing, feature selection, model training, hyperparameter tuning, evaluation, and deployment with a user-friendly interface for real-time predictions. Enabled multiple business use cases including demand forecasting, product recommendations, dynamic pricing, customer segmentation, inventory optimization, store analytics, and fraud detection.
Azure AI Fundamentals AI 900
Microsoft
June 1, 2026 – Present
Generative AI Fundamentals
Databricks
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from SLM development and AI agents to synthetic data generation and NL-to-SQL systems, indicates a broad interest and adaptability, which aligns well with innovative AI roles. The focus on 'product-based AI initiatives' and 'real-world applications' suggests a practical, impact-driven approach. The certifications in Azure AI Fundamentals and Generative AI Fundamentals demonstrate a commitment to continuous learning and staying current with industry trends, which is a positive cultural indicator for a dynamic AI engineering environment. The experience level (2 years) is consistent with an early-career professional with significant project contributions.
Soft Skills & Operational Fit
The candidate's resume highlights leadership in developing AI-based systems (e.g., QA Genie Pro) and being awarded 'Star Performer,' suggesting strong initiative, problem-solving, and delivery capabilities. The focus on 'high-impact, scalable AI solutions' indicates a results-oriented mindset. However, without direct assessment data on collaboration or communication in a team setting, a full evaluation of soft skills and operational fit is limited.