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AI Engineer with 2+ years in Large Language Models & MLOps
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI and Machine Learning Engineer with 2+ years of hands-on experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), OCR-driven document understanding, NLP, and semantic search. Experienced in building end-to-end LLM applications using GPT, BERT, T5, LLaMA, and Mistral, developing scalable ML pipelines, and deploying Flask-based services on AWS and GCP using Docker and Kubernetes.
Dr. NGP Institute of Technology
Bachelor of Engineering · Computer Science and Engineering
August 1, 2020 – June 30, 2024
KGISL Technologies Pvt Ltd
AI/ML Developer
April 1, 2024 – Present
India
Wetring Infotech Pvt Ltd
Software Developer
August 1, 2023 – December 31, 2023
India
RAG Chatbot System
June 24, 2026 – Present
Developed an LLaMA + FAISS based RAG chatbot improving contextual answer accuracy by 30 percent.
Semantic Search Engine
June 24, 2026 – Present
Built an embedding-based semantic search system using BERT and T5 with sub-second retrieval performance.
LLM Fine-Tuning Pipeline
June 24, 2026 – Present
Automated dataset preparation, training, evaluation, and model versioning using MLflow, reducing iteration cycles by 40 percent.
Cultural Fit Analysis
The candidate's experience in developing diverse AI applications (chatbots, semantic search, summarization, fine-tuning pipelines) and working with various technologies (AWS, GCP, Docker, Kubernetes, multiple LLMs) suggests adaptability and a broad interest in the AI domain. The quantifiable achievements in projects and professional experience indicate a results-oriented approach. The personal projects complement professional experience, showing initiative and continuous learning, which are positive indicators for cultural fit in an innovative environment.
Soft Skills & Operational Fit
The candidate's resume indicates collaboration with product and engineering teams, suggesting good teamwork and communication skills. The project descriptions highlight problem-solving and optimization efforts (e.g., reducing iteration cycles, improving accuracy, reducing latency), which are crucial for operational fit. However, without specific assessment data, a deeper analysis of soft skills like leadership, conflict resolution, or adaptability is not possible.