Machine Learning Engineer

Celex Technologies is looking for a hands-on Machine Learning Engineer to build intelligent systems for real-world manufacturing and operational problems.
You’ll work across areas such as computer vision, quality control, production optimization, process monitoring and operational automation.
The role involves owning problems end-to-end — understanding the operational need, working with available data, researching possible approaches, building and evaluating solutions, and deploying them for real-world use.
We're looking for someone who is comfortable figuring things out rather than someone who already knows every tool or technique required.

Responsibilities

1. Build ML and optimization solutions for manufacturing and operational use cases.
2. Work on problems involving computer vision, image/video analysis, OCR, forecasting, optimization and decision-making.
3. Analyze production and operational data to identify bottlenecks and opportunities for improvement.
4. Develop solutions for problems such as production planning, scheduling, resource allocation and process optimization.
5. Own projects from problem definition and data collection through research, implementation, evaluation and deployment.
6. Build the software and data pipelines required to turn models and algorithms into usable systems.
7. Evaluate solutions against real operational outcomes and improve them over time.
8. Work directly with manufacturing and operations teams to understand processes and identify opportunities for ML, optimization and automation.

Required / Preferred:

Strong Python and software engineering skills.
Solid understanding of machine learning, statistics and quantitative problem-solving.
Practical experience building ML or data-driven systems.
Ability to independently research and implement unfamiliar algorithms and techniques.
Comfortable working with messy, real-world datasets.
Ability to translate ambiguous business or operational problems into technical solutions.
Ability to take a project from experimentation to a usable implementation.

Added advantage, but not required:

Computer vision, OCR or video analytics experience.
Optimization, operations research, simulation or forecasting experience.
Experience deploying ML systems into production.
Cloud or GPU infrastructure experience.
Experience with manufacturing, logistics, supply chains, robotics or industrial automation.