Akash Kumar Gautam
Ai Engineer Nlp, Llm, Genai @Hochschule Anhalt
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WORK HISTORY
Ai Engineer Nlp, Llm, Genai @Hochschule Anhalt
Leipzig, DE
As a Generative AI Research Engineer, I contribute to the development of applied AI solutions for the real estate and finance domains. My key contributions include:• End-to-End Pipeline Development: Designed and implemented robust GenAI pipelines for automated, high-quality real estate expose generation and evaluation using prompting and in-context learning.• Evaluation & Benchmarking: Created a proprietary evaluation framework to systematically benchmark LLMs on domain-specific tasks, ensuring reliability and performance for financial and real estate applications.• Data Standardization: Pioneered the creation of unified data formats for complex datasets, facilitating seamless data distribution and collaboration across financial and real estate domains.• Model Optimization & Adaptation: Specialized in domain adaptation of language models through soft prompting and parameter-efficient methods to align outputs with precise domain requirements.• Technology Scouting & Integration: Continuously evaluate and integrate rapidly evolving LLM technologies (including vLLMs) to maintain state-of-the-art, efficient, and scalable AI infrastructure.
EDUCATION
Amity International School Mayur Vihar
Class 12th CBSE Board (94.00%)
Indraprastha Institute of Information Technology, Delhi
Bachelor of Technology, Computer Science and Engineering
Delhi Public School Indirapuram
Class 10th (10.00 CGPA)
Universität des Saarlandes
Master of Science - MS, Language Science and Technology
ABOUT AKASH KUMAR GAUTAM
Research Engineer & AI Engineer specializing in Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP). With a Master’s in Language Science and Technology from Universität des Saarlandes, I bring deep expertise in computational linguistics, domain adaptation, and applied AI research.I currently develop end-to-end Generative AI pipelines for real estate and financial applications—implementing in-context learning, Retrieval-Augmented Generation (RAG), and parameter-efficient fine-tuning to tailor LLMs for domain-specific use cases. My work includes systematic LLM evaluation, benchmarking, and model optimization using frameworks like vLLM and custom adaptation techniques.My background also includes full-stack software engineering at UnitedHealth Group, where I built scalable applications using React, TypeScript, Spring Boot, and data analytics pipelines. This strong engineering foundation enables me to build production-ready, robust, and efficient AI systems—bridging the gap between experimental research and deployable solutions.I have a strong publication record in top-tier AI conferences including NAACL, ACL, and ICWSM, with research spanning temporal expression topics like social computing, information retrieval, and, knowledge graph construction. Previously, I contributed to AI research at Bosch Center for AI and DFKI.
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