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About me (Registered since 02/02/2026)
AI/ML Engineer Lead with 2.5+ years of experience building and deploying production ML systems and generative AI agentic pipelines that deliver measurable business value. Proven track record of reducing operational costs by 40% and improving system performance by 43% through intelligent automation, LLM optimization, and scalable cloud architectures. Expertise spans the complete ML lifecycle from stakeholder collaboration and requirement gathering to model deployment, monitoring, and ROI demonstration.
Specialized in agentic workflow design using LangChain and LangGraph, with hands-on experience fine-tuning LLMs (Llama 3.1-8B) and deploying them across cloud and edge environments. Strong software engineering foundation with proficiency in Python, SQL, AWS cloud services, and containerization technologies. Successfully collaborated with cross-functional teams including data engineers, software engineers, and business stakeholders to translate complex requirements into scalable AI solutions.
At AI Seer, architected end-to-end fact-checking pipelines integrating vector databases and semantic retrieval systems, while at IBM managed critical sales operations applications serving enterprise financial planning workflows. Demonstrated ability to build reusable components, implement production monitoring systems, and drive user engagement improvements of 300% through measurable, data-driven AI solutions.
Skills
Tech Skills
Portfolio
Education
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January 2024 - May 2025
National University of Singapore
Master's
Singapore
National University of Singapore (NUS) Master of Computing in Artificial Intelligence | January 2024 - June 2025
Advanced graduate program specializing in AI systems and machine learning engineering. Focused on production AI applications including LLM pipeline development, retrieval-augmented generation (RAG), and scalable ML systems. Built hands-on expertise through projects in conversational AI with LangChain, vector databases for semantic search, model interpretability, and end-to-end ML deployment pipelines—directly applicable to building AI-powered production systems.
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July 2018 - July 2022
Thiagarajar College of Engineering
Bachelors
India
Thiagarajar College of Engineering (TCE) Bachelor of Engineering in Computer Science | July 2018 - June 2022
Comprehensive undergraduate program covering core computer science fundamentals including data structures, algorithms, database systems, software engineering, and web technologies. Built foundation in object-oriented programming, system design, and software development methodologies. Gained hands-on experience through academic projects in full-stack development, data analysis, and problem-solving, establishing technical proficiency across multiple programming languages and development frameworks.
Work Experience
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July 2025 - Present
AI Seer, Singapore
AI/ML Engineer Lead
I led the development of Facticity, an end-to-end AI-powered fact-checking platform, where I architected multi-step LLM pipelines that delivered measurable business value through cost optimization and automated workflows. My responsibilities included designing agentic systems using LangChain and LangGraph that integrated web scraping, citation filtering, and semantic retrieval, resulting in a 40% reduction in operational costs through intelligent caching and reusable component architecture. I collaborated closely with stakeholders to define success metrics and requirements, ensuring the platform met real-world fact-checking needs while maintaining high accuracy and reliability standards.
Additionally, I spearheaded a strategic partnership with Qualcomm AI to optimize LLM deployment for production environments, orchestrating complete model training pipelines on AWS SageMaker with systematic experiment tracking and performance monitoring. This involved fine-tuning Llama 3.1-8B models and successfully deploying them on edge devices, achieving 43% performance improvements through NPU acceleration while building scalable vector database systems for semantic similarity retrieval. My work demonstrated clear ROI through measurable user engagement improvements (tripling daily interactions to 300+) and established production-ready ML systems that could be monitored, scaled, and maintained across different deployment environments.
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January 2022 - November 2023
IBM, India
Software Developer
I took full ownership of the Quote to Cash application, a critical sales operations system that processed enterprise financial planning workflows and customer transactions using Node.js and Vue.js technologies. My responsibilities included developing and maintaining over 20 RESTful APIs that seamlessly integrated with IBM Sales Cloud backend services, ensuring robust data flow and system reliability for high-volume commercial operations. I collaborated extensively with business stakeholders to understand requirements and success metrics, translating complex sales process needs into scalable technical solutions while maintaining clean, maintainable code standards and software engineering best practices.
My role also encompassed significant ML engineering and production system optimization, where I developed custom Python monitoring tools featuring ML-based anomaly detection algorithms (Isolation Forest, ARIMA) to track and optimize AWS resource utilization across cloud environments. This resulted in a 20% reduction in cloud costs through automated remedial actions and intelligent resource management, while simultaneously reducing application downtime by 25% through proactive monitoring and alerting systems. I consistently focused on delivering measurable business impact by upgrading system performance, implementing CI/CD practices, and managing comprehensive AWS infrastructure (EC2, IAM, S3) to ensure secure, scalable deployment that supported enterprise-level sales operations and financial planning workflows.
