Highlights
Join a dynamic team, learn modern ML practices, and contribute to production-grade software.
Description
Job Summary
pJoin our AI-driven logistics and supply chain software team at Kaleris. As a Machine Learning Engineer, you’ll develop, test, and deploy advanced ML models to optimize operations and improve global supply chains.
Responsibilities
- Develop, test, and deploy machine learning models for core product features (classification, regression, NLP, RL).
- Build and maintain data pipelines for ingestion, cleaning, feature engineering, and labeling.
- Implement reproducible experiments and automate training/evaluation workflows.
- Contribute to CI/CD for ML services using containers and cloud environments.
- Monitor model performance, latency, and drift; assist in troubleshooting and incident response.
Required Skills
- Python
- Pandas
- Numpy
- Git
- Data Pipelines
Required Skills Explained
- Proficiency in Python and core ML/data libraries such as scikit-learn, pandas, and numpy.
- Familiarity with building and evaluating machine learning models and metrics.
- Knowledge of Git, unit testing, and basic software engineering practices.
- SQL experience for data manipulation.
- Exposure to PyTorch or TensorFlow and experiment tracking tools like MLflow.
- Experience with Docker, Kubernetes, and major cloud platforms such as Azure, AWS, or GCP.
Who is this for
pEnthusiastic ML engineers with a passion for solving complex logistics problems and contributing to cutting-edge AI solutions.
Why This Job is a Good Opportunity
ulliOpportunity to work on mission-critical logistics and supply chain software used by leading operators worldwide.liPotential for mentorship and growth within the AIML team with diverse project exposure.liCompetitive pay, comprehensive benefits, and an inclusive company culture.liInvolvement in cutting-edge AI systems that directly impact real-world operations.
Interview Preparation Tips
- Prepare examples of projects or work experience where you have used Python for data analysis or built machine learning models.
- Practice explaining the steps involved in building and evaluating a machine learning model, including feature engineering and model validation techniques.
- Familiarize yourself with Git commands and version control best practices.
- Review Kubernetes and Docker basics as they relate to CI/CD for ML services.
Career Growth in This Role
pThis role offers numerous opportunities for career advancement within the AIML team. You can expect to gain expertise in modern ML/MLOps practices and contribute to production-grade solutions. Additionally, the diverse project exposure allows for skill diversification and specialization in areas like NLP or reinforcement learning. Kaleris emphasizes mentorship and professional growth, providing clear pathways for development as you become more proficient in your role.
Explore More Opportunities
Skills
Frequently Asked Questions
What experience is preferred?Experience with PyTorch or TensorFlow, and exposure to Docker, Kubernetes, and major clouds (Azure/AWS/GCP) are preferred.
Is this role suitable for internships?Yes, the role welcomes candidates with relevant internship experience.
What kind of mentorship is available?Kaleris offers extensive mentorship and growth opportunities within our AIML team.
