Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Generative AI & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated AI/ML Engineer Intern with 8 months of hands-on experience in developing and deploying production-grade Generative AI systems. Proven ability in integrating LLM-driven solutions, building scalable backend systems, and designing advanced RAG pipelines. Possesses strong technical skills in Python, TensorFlow, PyTorch, LangChain, and Neo4j, with a focus on delivering innovative solutions in AI, machine learning, and data engineering domains.
Guru Gobind Singh Indraprastha University, Delhi
Bachelor of Technology · Industrial Internet of Things
August 1, 2023 – June 30, 2027
Dr. Virendra Swarup Public School, Kanpur
Class 12 (ISC)
N/A – May 31, 2022
Arrdas Studio
AI/ML Intern
November 1, 2025 – January 31, 2026
New York City, New York, United States
NIT Kurukshetra
Research Intern
May 1, 2025 – June 30, 2025
India
IIT Indore
Research Intern
May 1, 2025 – July 31, 2025
India
Cultural Fit Analysis
The candidate's profile shows a strong alignment with an innovative and research-driven culture, evidenced by multiple research internships and participation in a national hackathon. Their work on diverse AI applications, from electricity billing to multilingual translation and exam generation, suggests a broad interest in applying AI to various problems. The use of modern tools and frameworks (e.g., LangChain, Neo4j, Streamlit) indicates a proactive approach to adopting new technologies, which is beneficial for a dynamic AI engineering role. The candidate's academic background in Industrial Internet of Things further broadens their perspective on real-world applications of AI.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in designing and deploying complex AI systems. Their experience in automating workflows and optimizing processing times suggests an operational mindset focused on efficiency. The diversity of their internship experiences indicates adaptability and a willingness to learn new domains. While direct evidence of team collaboration is limited to project descriptions, the nature of their internship roles implies working within a structured environment.