Job Description:

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 protected 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.




Robust 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
Reinforcement learning exposure (policy learning, reward design, evaluation) and/or simulation (discrete-event or agent-based).
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.
Collabo

📌 Software Engineer Chennai
🏢 Kaleris
📍 Chennai