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AI Research Engineer with less than a year in Machine Learning & Statistical Modeling
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computational physicist (PhD, IISER Bhopal) with expertise in stochastic modelling, large-scale simulation, and rigorous statistical analysis, now building applied AI/ML engineering skills at an AI startup. Fluent in deriving analytical theory and validating it against data – an approach that maps naturally to model diagnostics, quantitative research, and data science pipelines. Four peer-reviewed publications in high-impact journals; international research collaboration across India and Germany.
Indian Institute of Science Education and Research (IISER) Bhopal
Ph.D. · Physics
August 1, 2019 – April 1, 2026
Indian Institute of Science Education and Research (IISER) Bhopal
B.S.-M.S. · Physics (Integrated)
January 1, 2014 – January 1, 2019
Conscious Engines
Research Intern - AI/ML
June 1, 2026 – Present
Bengaluru, Karnataka, India
IISER Bhopal & Forschungszentrum Jülich
Doctoral Researcher
August 1, 2019 – April 1, 2026
Germany
Cultural Fit Analysis
The candidate's background in academic research, particularly in physics, suggests a culture of deep inquiry, problem-solving, and rigorous validation. The recent shift to an AI/ML research internship indicates a strong interest in applying theoretical knowledge to practical, cutting-edge problems, which aligns well with an innovative AI research environment. The diversity of projects, from theoretical physics simulations to LLM benchmarking, shows a broad intellectual curiosity and adaptability. The international collaboration experience also points to an ability to work in diverse teams.
Soft Skills & Operational Fit
The candidate's profile indicates strong analytical thinking, rigorous research methodology, and the ability to work on complex, large-scale problems. Experience with international research collaboration and oral presentations suggests good communication and teamwork potential. The transition from computational physics to applied AI/ML demonstrates adaptability and a proactive learning attitude. However, without specific psychometric or English test results, a full assessment of soft skills and operational fit is limited.