Job Description:
About Kaleris and the Role
Lead the architecture and delivery of scalable ML/RL systems at Kaleris. As a Senior AIML Software Engineer, you’ll set technical standards, mentor engineers, and drive initiatives from problem framing through production operations, shaping AI solutions that materially improve throughput, cost, and service reliability for global supply chains.
What You’ll Do
- Architect end-to-end ML/RL platforms: pipelines, training/evaluation environments, model serving, monitoring, and incident response.
- Establish data quality standards, observability, and governance across ML products.
- Optimize production workflows for performance, latency, scalability, and security.
- Lead technical reviews, elevate code/design quality, and ensure reproducible experimentation.
- Partner cross-functionally to translate business goals into model objectives, success metrics, and deployment strategies.
- Mentor and coach engineers and data scientists; promote best practices and continuous improvement.
Minimum Qualifications
- Bachelor’s/Master’s/PhD in Computer Science or related field; 5–8+ years building and operating ML systems in production.
- Advanced proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
- Experience building enterprise applications with Java and Spring Boot.
- Expert-level experience with scikit-learn, pandas, numpy, and machine learning model development and evaluation.
- Proven track record deploying on Azure/AWS/GCP; strong with Docker, Kubernetes, and CI/CD.
- Hands-on experience with monitoring, drift detection, alerting, and incident response for ML services.
Preferred Qualifications
- Simulation expertise (discrete-event or agent-based), queueing/stochastic modeling, and reinforcement learning in real-world settings.
- MLOps depth: model versioning, experiment tracking, automated retraining, governance.
- Logistics/supply chain domain experience and optimization in complex/partially observable environments.
- Experience with serverless functions and event-driven architectures for low-latency model APIs, including Knative, Azure Functions, and AWS Lambda
Why Kaleris
- Own strategic ML initiatives with direct line-of-sight to customer impact.
- Influence product direction and technical standards in a modern AI/ML organization.
- Competitive salary and benefits; inclusive culture; opportunities to grow scope and leadership.
Kaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Skills Required
- Bachelor's/Master's/PhD in Computer Science or related field
- 5-8+ years building and operating ML systems in production
- Advanced proficiency in Python and deep learning frameworks (PyTorch or TensorFlow)
- Expert-level experience with scikit-learn, pandas, numpy, and machine learning model development and evaluation
- Proven track record deploying on Azure/AWS/GCP
- Strong experience with Docker, Kubernetes, and CI/CD
- Hands-on experience with monitoring, drift detection, alerting, and incident response for ML services
- Simulation expertise (discrete-event or agent-based), queueing/stochastic modeling, and reinforcement learning
- MLOps depth: model versioning, experiment tracking, automated retraining, governance
- Logistics/supply chain domain experience and optimization in complex/partially observable environments
- Experience with serverless functions and event-driven architectures, including Knative, Azure Functions, AWS Lambda
What We Do
Kaleris is a leading provider of cloud-based supply chain execution and visibility technology solutions. Many of the world's largest brands rely on Kaleris to provide mission-critical technology for yard management, transportation management, maintenance and repair operations, terminal operating systems, and ocean carrier and vessel solutions. By consolidating supply chain execution software assets across major nodes and modes, we address the dark spots and data gaps that cause friction and inefficiency in the global supply chain.
