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AI Engineer with 1+ years in LLM & ML Systems
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Assessing your cultural and operational fit
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
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).
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).
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.
SLM Development
June 1, 2026 – Present
Built domain-specific SLMs for product classification and recommendations. Integrated TruLens for model evaluation (fairness & explainability).
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.
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.
Generative AI Fundamentals
Databricks
June 1, 2026 – Present
Azure AI Fundamentals AI 900
Microsoft
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.