Job Responsibility:

- Mathematical Formulation & Modelling: Translate vague, complex business problems into clean mathematical frameworks, cost functions, and optimization problems.
- Architecture & Implementation: Build, train, and fine-tune classical machine learning models and deep neural networks from the ground up using Python and core ML libraries.
- Algorithm Debugging & Optimization: Diagnose performance bottlenecks including but not limited to vanishing/exploding gradients, data leakage, and overfitting by evaluating model behaviours at the mathematical and tensor levels.
- Production Deployment: Write modular, well-tested, and optimized Python code to deploy models into production environments with low latency and high reliability.
- Hands-On Prompt Engineering & LLM Systems: Craft, test, and systemize advanced prompt strategies and integrate them into production LLM orchestration pipelines.

Key Requirements 1) Mathematical Logic & Deep Learning Fundamentals (Non-Negotiable)

- First-Principles Understanding: Deep, intuitive grasp of linear algebra, multivariable calculus, probability theory, optimization algorithms (SGD, Adam, L-BFGS), and statistical inference.
- Neural Network Internals: Clear conceptual understanding of backpropagation mechanics, attention mechanisms, normalization layers, loss function formulation (e.g., custom Huber, focal, or loss surface regularization), and architectural trade-offs.




- No "Black Box" Engineering: Ability to explain why a model behaves the way it does, debug mathematical anomalies in training runs, and select architectures based on mathematical suitability rather than hype.

2) Hands-On Python Mastery (Non-Negotiable)

- Advanced proficiency in Python (Beyond the usual Pandas/ NumPy/ SciPy/ Scikit- Learn). Strong software engineering practices: modular design, vectorized operations, energetic programming, performance profiling, and write-once clean code.

3) Prompt Engineering & LLM Integration (Bonus Skill)

- Demonstrated experience in prompt optimization, guardrailing, dynamic prompt templating, and managing context window constraints.

Education & Qualifications

- This role is for experienced ML/ AI practitioners.
- While domain knowledge/ training for the diamond industry would be provided as needed, you are expected to be already skilled with developing ML/ AI solutions.
- A degree is quantitative sciences would be a bonus, although not a mandatory requirement.

Why join us? We value innovation, creativity, and collaboration. We offer a supportive and dynamic work environment where you can grow your career while making a real impact. If you’re looking for a challenging and rewarding role, we’d love to hear from you!

Pay: From ₹25,000.00 per month

Work Location: In person

📌 Analytics Intelligence ML / AI (Ichchhapor)
🏢 Hari Krishna Exports
📍 Ichchhapor