Hire Developar
Budget: ₹75,000 – ₹150,000 INR
I’m assembling the early engineering team for Ednarex, an R&D-driven startup intent on shipping AI-powered products that tackle real-world problems at global scale. My immediate priority is an AI/ML Engineer who can take concepts from whiteboard sketches to a running service.
You will design and train models, validate them against business KPIs, and work side-by-side with product and cloud engineers to deploy your work. Everything is green-field, so you’ll have freedom in approach provided you deliver clean, maintainable code.
Must-haves
• Deep command of Python for data processing, experimentation, and micro-service development (our core language).
• Practical experience with TensorFlow, PyTorch, or a comparable framework, plus a solid grasp of feature engineering, experiment tracking, and MLOps principles.
• Comfort with cloud services (AWS, GCP, or Azure) and containerisation so your models ship smoothly.
Deliverables
1. End-to-end prototype of at least one core model, complete with well-documented Python code.
2. Concise technical write-up detailing your architecture choices, evaluation metrics, and recommended next steps.
Acceptance criteria
• Reproducible training pipeline that runs on a standard GPU instance.
• Model reaches the jointly agreed baseline on validation data.
• Codebase passes linting and the unit test suite provided during onboarding.
If this sounds like the challenge you’ve been waiting for, send over a short note about a recent ML project you owned, any public code links, and your earliest start date.
Let’s push the boundaries of what AI can solve—together.
You will design and train models, validate them against business KPIs, and work side-by-side with product and cloud engineers to deploy your work. Everything is green-field, so you’ll have freedom in approach provided you deliver clean, maintainable code.
Must-haves
• Deep command of Python for data processing, experimentation, and micro-service development (our core language).
• Practical experience with TensorFlow, PyTorch, or a comparable framework, plus a solid grasp of feature engineering, experiment tracking, and MLOps principles.
• Comfort with cloud services (AWS, GCP, or Azure) and containerisation so your models ship smoothly.
Deliverables
1. End-to-end prototype of at least one core model, complete with well-documented Python code.
2. Concise technical write-up detailing your architecture choices, evaluation metrics, and recommended next steps.
Acceptance criteria
• Reproducible training pipeline that runs on a standard GPU instance.
• Model reaches the jointly agreed baseline on validation data.
• Codebase passes linting and the unit test suite provided during onboarding.
If this sounds like the challenge you’ve been waiting for, send over a short note about a recent ML project you owned, any public code links, and your earliest start date.
Let’s push the boundaries of what AI can solve—together.