10/16/2026 updated
SK
100 % available
PhD Physicist & AI Software Engineering Subject Matter Expert
Banglore, India
Only remote
Ph.D. in Physics (Computational Focus)Software as a Service
Software Engineering & Code Analysis
Expertise in code review and debugging, production-level Python, algorithmic efficiency, data structures, system design, object-oriented programming (OOP), and unit testing. Proven ability to debug production-level Python architectures and identify complex edge-case failure modes, structural bugs, and optimization bottlenecks.
AI Training & LLM Evaluation
Deep specialization in Reinforcement Learning from Human Feedback (RLHF), SFT dataset creation, LLM code assessment, prompt engineering (adversarial and Chain-of-Thought), benchmarking optimization, and hallucination diagnostics. Extensive experience training frontier Large Language Models via RLHF.
Physics & Computational Modeling
Strong foundation in quantum mechanics, statistical mechanics, electromagnetic wave simulations, and numerical analysis using NumPy and SciPy. Background spans advanced physical science paradigms bridged with robust software engineering practices.
Python Programming
Proficiency in production-grade Python syntax for building automated multi-step reasoning frameworks, simulation scripts, and data engineering pipelines.
Adversarial Red-Teaming
Design of complex, high-difficulty coding and mathematical reasoning benchmarks to identify logical bugs, structural blind spots, and attention-mechanism vulnerabilities in frontier LLMs.
Dataset Architecture & Annotation
Standardization of high-quality dataset annotation pipelines and writing of ground-truth, executable code solutions using Chain-of-Thought (CoT) workflows to train models on advanced technical tasks.
Technical Stack
Hands-on experience with Python, C++, LaTeX, Git/Version Control, Linux, and technical documentation.
Prompt Engineering
Advanced prompt engineering including adversarial and Chain-of-Thought strategies, used to enforce structural unit testing and dimensional verification in AI tutoring models.
Simulation Development
Development and execution of theoretical boundary condition check scripts in Python to run electromagnetic wave simulations, isolating critical edge cases and experimental deviations.
Expertise in code review and debugging, production-level Python, algorithmic efficiency, data structures, system design, object-oriented programming (OOP), and unit testing. Proven ability to debug production-level Python architectures and identify complex edge-case failure modes, structural bugs, and optimization bottlenecks.
AI Training & LLM Evaluation
Deep specialization in Reinforcement Learning from Human Feedback (RLHF), SFT dataset creation, LLM code assessment, prompt engineering (adversarial and Chain-of-Thought), benchmarking optimization, and hallucination diagnostics. Extensive experience training frontier Large Language Models via RLHF.
Physics & Computational Modeling
Strong foundation in quantum mechanics, statistical mechanics, electromagnetic wave simulations, and numerical analysis using NumPy and SciPy. Background spans advanced physical science paradigms bridged with robust software engineering practices.
Python Programming
Proficiency in production-grade Python syntax for building automated multi-step reasoning frameworks, simulation scripts, and data engineering pipelines.
Adversarial Red-Teaming
Design of complex, high-difficulty coding and mathematical reasoning benchmarks to identify logical bugs, structural blind spots, and attention-mechanism vulnerabilities in frontier LLMs.
Dataset Architecture & Annotation
Standardization of high-quality dataset annotation pipelines and writing of ground-truth, executable code solutions using Chain-of-Thought (CoT) workflows to train models on advanced technical tasks.
Technical Stack
Hands-on experience with Python, C++, LaTeX, Git/Version Control, Linux, and technical documentation.
Prompt Engineering
Advanced prompt engineering including adversarial and Chain-of-Thought strategies, used to enforce structural unit testing and dimensional verification in AI tutoring models.
Simulation Development
Development and execution of theoretical boundary condition check scripts in Python to run electromagnetic wave simulations, isolating critical edge cases and experimental deviations.
Languages
EnglishFluent
Project history
Evaluate, audit, and optimize AI-generated code and multi-step programming pipelines for runtime correctness, computational efficiency, and structural alignment with engineering best practices. Design adversarial coding and mathematical reasoning benchmarks to identify logical bugs and vulnerabilities in frontier LLMs. Standardize high-quality dataset annotation pipelines using Chain-of-Thought workflows. Grade and align model outputs based on multidimensional criteria including syntax validation, dimension consistency, memory constraints, and core physical/mathematical laws. Collaborate on domain-specific data engineering pipelines to eliminate hallucination loops in complex simulation models and scientific algorithms.
Created a comprehensive benchmarking dataset consisting of 500+ complex, graduate-level coding prompts and physics challenges explicitly tailored to pressure-test model reasoning limits. Authored clean, optimized master solution keys using production-grade Python syntax and LaTeX notations to establish strict ground-truth training paths for Supervised Fine-Tuning (SFT).
Systematically analyzed mathematical and runtime breakdowns in automated AI tutoring models, creating prompt frameworks that enforce structural unit testing and dimensional verification prior to final execution.