Applied AI Engineer with 4+ years in RAG Pipelines & Event-Driven Architectures
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Engineer with 4+ years building production backend systems and 1 year delivering applied AI/ML applications. Hands-on experience building RAG pipelines, integrating AI services and APIs into enterprise applications, and designing event-driven, workflow-automated architectures. Strong in performance tuning, cost optimization, and secure deployment using Python, Java, FastAPI, Spring Boot, Kafka, Neo4j, Docker, and AWS, translating solution designs into working systems from POC through production.
IIIT Tiruchirappalli
M.Tech · CS&E
August 1, 2024 – June 30, 2026
KIIT University
B.Tech · IT
August 1, 2015 – June 30, 2019
IIT Madras iACE Lab
AI/ML Research Intern - Industrial Incident Intelligence RAG Platform
February 1, 2026 – May 1, 2026
India
IIT Madras iACE Lab
AI/ML Research Intern — P&ID Knowledge Graph Generation Platform
December 1, 2025 – May 1, 2026
India
MakeMyTrip
Senior Software Engineer I
May 1, 2022 – March 1, 2023
India
Alepo Technologies
Engineer R&D
July 1, 2021 – May 1, 2022
India
Zycus Infotech
Executive to Associate Software Engineer
July 1, 2019 – July 1, 2021
India
Pothole Detection & Civic Reporting Platform
February 1, 2025 – Present
Designed an event-driven Kafka architecture decoupling complaint ingestion, geospatial routing, AI inference, and notifications, enabling independent scalability and fault isolation; deployed on AWS EC2 with PostGIS spatial matching. Integrated a YOLOv8s computer vision pipeline trained on 7,000+ annotated road images for automated pothole detection and severity classification.
NLP
NPTEL (IIT Kharagpur)
June 1, 2026 – Present
From P&IDs to Knowledge Graphs: Enabling Process Intelligence Through Digitisation
ICIDTSM 2026, IIT Madras iACE Lab
January 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from civic reporting to industrial incident intelligence and P&ID knowledge graphs, showcases adaptability and a broad interest in applying AI across different domains. Their experience in both full-stack engineering and specialized AI/ML research roles indicates a versatile skill set. The continuous pursuit of higher education (M.Tech) and certifications (NLP, publication) reflects a strong commitment to professional growth and a proactive approach to skill development, which is a positive indicator for cultural fit in an innovative environment. The blend of academic research and industry experience suggests a practical, application-focused mindset.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong problem-solving skills through performance optimization and security remediation. Their experience in owning features end-to-end and improving customer satisfaction indicates a results-oriented and responsible work attitude. The involvement in research internships and publications suggests a proactive and continuous learning mindset, which aligns well with the dynamic nature of an Applied AI Engineer role. The detailed descriptions of technical challenges and solutions imply good communication of complex technical concepts.