About Kaleris and the Role

As an AIML Software Engineer at Kaleris, you’ll design, build, and maintain production ML systems that power decision-making across logistics and supply chain products. Partner with data scientists and product teams to deliver scalable model training, serving, monitoring, and continuous improvement.

What You’ll Do

• Own end-to-end ML workflows: data ingestion, feature engineering, training, validation, deployment, and observability.
• Implement robust model serving and APIs; ensure reliability, performance, and security.
• Develop simulators/training environments for safe evaluation of model behavior.
• Automate CI/CD pipelines for ML using containers and cloud-native tooling.
• Monitor model health, detect drift, run A/B tests, and support automated retraining.
• Write clean, well-tested code; contribute to requirements, design, and peer reviews.

Minimum Qualifications

• Bachelor’s/Master’s in Computer Science or related field; 2–4 years of ML/software engineering experience.
• Strong Python skills with hands-on experience using scikit-learn, pandas, numpy, and machine learning algorithms.
• Experience with PyTorch or TensorFlow, SQL, and data wrangling at scale.
• Proficiency with Git, unit/integration testing, and CI/CD.
• Experience deploying to Azure/AWS/GCP with Docker and Kubernetes.

Preferred Qualifications

• Hands-on experience building enterprise applications with Java and Spring Boot.
• Experience with model governance, monitoring, and automated retraining.
• Domain knowledge in logistics/supply chain operations.
• Hands-on experience with serverless functions for model serving and event-driven data processing, such as Knative, Azure Functions, and AWS Lambda (Amazon Lambda).

Why Kaleris

• High-impact ML work at the frontier of decision intelligence for the supply chain.
• Collaborative, global team; clear career progression and technical leadership opportunities.
• Competitive compensation, benefits, and a culture that values inclusion and craftsmanship.