KlearNow is achieving an unprecedented transformation in the growth of our businesses, rethinking the way we engage with customers and partners, and how the world’s trade flows across our global network.
Why us
Be a part of a rapidly growing, series A funded company where you will have the opportunity to extend our leadership position and fast-track innovation behind AI-powered intelligent supply chain solutions.
KlearNow has the flexibility of a small start up with the security of a well-funded organization with strong backers and advisors.
We are looking for an experienced AI/ML Engineer who is self-driven and result oriented, a team player and able to work across teams.
Responsibilities
- Develop innovative AI solutions for challenging business problems.
- Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”.
- Build backend systems that interact with other microservices.
- Train and optimize machine learning models.
- Engineer machine intelligence systems and infrastructure for real-world production use at scale.
- Explore data features and manipulate data with SQL and scripts.
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like Scikit-learn)
- Should be able to understand the problem statement and be able to justify the usage of AI/ML as a solution
- Be able to quickly prototype and provide results
- Be able to understand requirements and fine tune them to provide results for concrete problems
Key Qualifications
- Strong coding experience in Python and working with related libraries (NumPy/Pandas/matplotlib).
- Familiarity with machine learning frameworks (Keras/PyTorch/Tensorflow) and relevant libraries.
- Excellent knowledge and good practical skills in major ML algorithms as applied to Natural Language Processing and Computer Vision.
- Practical experience with deep learning projects is highly valued.
- Ability to improve the accuracy, runtime, scalability and reliability of machine intelligence systems.
- Strong in understanding underlying math and algorithms.
- Passion for continuing to learn state-of-the-art techniques in ML/Data science.