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Backend Engineer with 2+ years in Applied AI & LLM Systems
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Backend engineer who ships production AI systems. Built FastAPI services handling 135K+ requests/day with zero failures and 34.5ms response time-experienced in LLM pipelines, RAG workflows, vector search, and rate-limited integrations.
Modern Education Society's College of Engineering, Pune
Bachelor of Engineering · Computer Engineering (Honors in Data Science)
August 1, 2019 – June 30, 2023
Tata Consultancy Services (Client: Axis Max Life Insurance)
Assistant System Engineer – Backend & ML Systems
May 1, 2024 – Present
India
AutoHub - AI-Powered Backend Automation Platform
January 1, 2026 – June 1, 2026
• Architected an end-to-end LLM pipeline to automatically discover, extract, and structure vehicle specification data from unstructured PDF brochures using the Google Gemini API, processing 10+ PDFs daily within API rate limits. • Engineered prompt workflows for structured JSON output extraction from documents, handling edge cases and format inconsistencies with validation and normalization layers. • Built FastAPI-driven orchestration services managing asynchronous task workflows, rate limit handling, checksum verification, and reproducible processing pipelines. • Designed a PostgreSQL relational schema with version tracking and traceability, enabling structured storage and query optimization for extracted datasets.
Multilingual Sign Language Recognition System
August 1, 2022 – June 1, 2023
• Designed an end-to-end computer vision system for multilingual sign language recognition using CNN-based architectures in TensorFlow/Keras. • Built structured data preprocessing, augmentation and training workflows for ASL, ISL, and RSL datasets, achieving 95% classification accuracy. • Developed modular inference components suitable for REST API integration and real-time deployment. • Led a cross-functional team coordinating model development, experimentation, validation processes, and UI demonstration layer.
FastAPI with OAuth and JWT Authentication
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from AI-powered backend automation to multilingual sign language recognition, indicates a broad interest and adaptability. The role as 'Assistant System Engineer – Backend & ML Systems' aligns well with the target 'Backend Engineer' role, especially given the emphasis on ML systems. The combination of academic and personal projects, alongside professional experience, suggests a proactive and continuous learning mindset, which is a good cultural fit for dynamic technical environments.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through the design of robust API middleware and modular architectures. Leadership experience is evident from leading a cross-functional team in the Multilingual Sign Language Recognition System project. The focus on production-grade systems, monitoring, and disaster recovery indicates a strong operational mindset. The detailed descriptions of API performance metrics and failure rate reduction highlight a results-oriented approach.