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AI Engineer with 1+ years in Generative AI & NLP
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Assessing your cultural and operational fit
Results-driven Generative AI and Machine Learning Engineer with hands-on experience designing NLP pipelines, RAG-based architectures, LLM-powered applications, and voice AI systems. Proficient in Python, FastAPI, LangChain, LangGraph, TensorFlow, and Docker. Proven ability to build, containerize, and deploy production-grade AI REST APIs on AWS with CI/CD pipelines. Experienced in prompt engineering, speech recognition (STT/TTS), conversational AI workflows, and agile cross-functional collaboration.
Rajasthan Technical University
Master of Computer Applications (MCA)
August 1, 2023 – June 30, 2025
Rajasthan University
Bachelor of Science
August 1, 2020 – June 30, 2023
Regex Software Services
AI/ML Engineer Intern
March 1, 2025 – March 1, 2026
India
Kentiq AI Voice Banking Assistant
June 24, 2026 – Present
Architected and delivered a full-stack voice-enabled conversational AI banking assistant for Dubai Bank using Whisper (STT) and gTTS (TTS), achieving ~92% speech recognition accuracy across varied accents and noise conditions. Engineered multi-step voice NLP workflows for 4 banking operations - balance inquiry, money transfer, cheque validation (OpenCV), and KYC - reducing simulated transaction completion time by ~50% vs. manual input flows. Implemented robust error handling for unclear speech, background noise, and invalid commands, with fallback voice prompts improving user interaction success rate to over 90% in testing.
BrainBite AI - Smart Food Recommendation Platform
June 24, 2026 – Present
Developed an ML-based real-time food recommendation engine using Scikit-learn and FastAPI, delivering personalised recommendations with <200ms response time, backed by a MySQL database and JavaScript frontend.
Voice-Enabled AI Assistant (STT & TTS)
June 24, 2026 – Present
Built a context-aware voice AI assistant integrating LangChain, Vector Database (FAISS), and LLMs for RAG-based responses, achieving seamless speech-to-speech interaction via Whisper STT and gTTS TTS with ~88% intent detection accuracy.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and hands-on experience in the AI domain, aligning well with an AI Engineer role. The diversity of projects (voice banking, food recommendation, general voice assistant) shows a breadth of application interests within AI. The internship experience at Regex Software Services further solidifies their commitment to the field. However, all projects are listed as 'personal', which might indicate less experience in collaborative, large-scale enterprise environments, potentially impacting cultural fit for highly structured team settings without further validation.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting good problem-solving skills. The mention of 'agile cross-functional collaboration' in the professional summary, though not explicitly detailed in experience, points towards an understanding of modern development methodologies. The focus on user interaction success rates and reduced transaction times in projects suggests a results-oriented approach.