Hiring For MLOps Engineer (JAX, PyTorch, Pallas/Triton) 🚀

🌍 Location: Remote 
💼 Employment Type: W-2 Contingent Role
💰 Compensation: ₹3,000 – ₹4,000/hr (approx.)
⏳ Commitment: 30–40 hours/week (Weekdays)

📖 Job Overview

Join a leading AI lab’s cutting-edge Generative AI team and contribute to building next-generation foundational AI models from the ground up. We are seeking highly skilled MLOps Engineers with deep expertise in modern machine learning frameworks, particularly JAX, PyTorch and kernel-level programming using Pallas/Triton.

In this role, you will collaborate closely with research and engineering teams to improve AI model performance, training infrastructure, distributed systems and ML framework-level optimization.

🔹 Key Responsibilities :-

Guide research and engineering teams to improve AI model performance in MLOps, training infrastructure and ML framework-level systems.
Design challenging domain-relevant tasks and provide accurate, well-structured solutions to ML and MLOps problems.
Evaluate MLOps solutions and provide clear written technical feedback.
Develop guidelines, rubrics, and evaluation frameworks for :-
* Training pipeline design
* Distributed systems reasoning
* Kernel-level optimization
Collaborate with subject matter experts to ensure consistency and quality in training data.
Support infrastructure optimization for large-scale AI model training workflows.

✅ Core Qualifications :-

2+ years of professional experience in:
ML infrastructure
MLOps
ML systems engineering
Hands-on production experience with:
JAX
PyTorch
Experience writing or optimizing custom GPU kernels using:
Pallas (JAX)
Triton

Strong understanding of :-

Distributed systems
Training pipelines
AI model optimization
Excellent written communication and technical explanation skills.
Ability to work at least 30 hours/week during weekdays.
Demonstrated professional growth and career progression.

⭐ Preferred Skills :-
Experience working with large-scale AI/GenAI systems.
Familiarity with ML framework internals and performance tuning.
Strong debugging and systems-level optimization capabilities.
Experience collaborating with research and engineering teams in AI environments.