AI Engineer with less than a year in LLMs & Cloud-Integrated Pipelines
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with hands-on experience building production-grade agentic AI systems, multi-agent architectures, and cloud-integrated pipelines. Co-authored a published research paper on a Multi-Agent Hybrid Architecture (MAC) for causally-grounded, auditable AI reasoning – directly aligned with agentic AI development. Currently building real-world AI workflows at Taxina using AWS, Python, and REST APIs. Strong foundations in LLMs, prompt engineering, and autonomous agent design patterns. Available to join immediately.
Kalasalingam Academy of Research and Education
B.Tech · Computer Science & Engineering (AI & ML)
August 1, 2022 – June 30, 2026
Taxina
AI Engineer Intern
January 1, 2026 – Present
Chennai, Tamil Nadu, India
SmarDTV Global Technology
Software Development Intern
May 1, 2024 – July 1, 2024
Bengaluru, Karnataka, India
MAC Framework - Multi-Agent Agentic AI System
January 1, 2026 – June 1, 2026
Designed and implemented a multi-agent pipeline with specialised agents handling knowledge retrieval, causal reasoning, and strategic output generation – implementing agent-to-agent handoff, tool routing, and structured response synthesis. Built auditable agent reasoning chains using causal grounding techniques, enabling traceable decision paths across the multi-agent workflow – directly applicable to RAG and agentic pipeline reliability. Evaluated agent accuracy, latency, and output reliability across multiple LLM configurations, applying systematic prompt engineering and agent reasoning patterns to improve consistency.
Real-Time Sign Language Recognition - CV + NLP Pipeline
January 1, 2025 – December 31, 2025
Built a modular AI inference pipeline with 92% gesture recognition accuracy, integrating computer vision and NLP stages with clean architecture for REST API-based downstream consumption. Implemented structured error handling and model evaluation layers, optimising prediction reliability and processing speed across the end-to-end pipeline.
Graduate McKinsey Forward Program
McKinsey & Company
June 1, 2026 – Present
Java Programming Certification
CodeChef
June 1, 2026 – Present
Microsoft Azure AI Fundamentals (AI-900)
Microsoft
June 1, 2026 – Present
Finalist IBM National Hackathon 2025
IBM
January 1, 2025 – Present
IEEE ICSTSN 2025
IEEE Xplore
January 1, 2025 – Present
The candidate scored 88% on the 'Junior Java Developer | Flights Manager Application' test, indicating a strong command of Java programming, testing frameworks, and clean code principles.
Strengths
Cultural Fit Analysis
The candidate's diverse project portfolio, including academic research in multi-agent systems and practical internships, demonstrates a broad interest and adaptability. Their engagement in hackathons and certifications, along with leadership experience, indicates a proactive and team-oriented mindset. The target role of AI Engineer aligns well with their academic background and professional experience, particularly in agentic AI and cloud-integrated pipelines.
Soft Skills & Operational Fit
The candidate's involvement in leadership roles (Fine Arts Club) and participation in hackathons and certifications suggest a proactive attitude, ability to collaborate, and a drive for continuous learning. The psychometric test score of 314/500 indicates average performance in logical reasoning, work attitude, stress handling, and team collaboration, which could be an area for further exploration during interviews. The English test score of 75/100 suggests good communication clarity.
Limitations