Kaleris Off Campus 2026 : Kaleris, a leading global provider of cloud-based supply chain execution and visibility technology, is inviting applications for its 2026 Off-Campus Drive. They are actively hiring highly skilled freshers for the Associate AIML Software Engineer role to join their AI/ML and analytics team at their massive engineering hub in Chennai.
If you are passionate about Artificial Intelligence and Machine Learning and want to build models that optimize real-world global logistics—from massive shipping ports to rail networks—this is the perfect launchpad for your career in Data Science and AI!
About Kaleris :
Kaleris builds mission-critical logistics and supply chain software used by leading operators worldwide. Many of the world’s largest brands rely on Kaleris (and its flagship product, Navis) to provide technology for yard management, transportation management, and terminal operating systems (TOS).
Kaleris is addressing the inefficiencies in the global supply chain by consolidating its supply chain execution software assets, filling in the gaps and eliminating dark spots in data. By joining their AIML team in Chennai, you will be launching your engineering career by working on real-world AI systems. These systems optimize terminal throughput, routing, and decision-making intelligence on a large, global scale.
Job Description :
As an Associate AIML Software Engineer, you will serve as a link between data science and software engineering. Your role will involve more than just building models in Jupyter notebooks; you will be responsible for developing, deploying, and maintaining production-grade machine learning pipelines that enhance decision-making across Kaleris’s logistics products.
| Company | Kaleris (Navis) |
| Role | Associate AIML Software Engineer |
| Location | Chennai, Tamil Nadu |
| Batch Eligible | 2026 / 2025 |
| Experience | Freshers (0 – 2 Years) |
| Expected CTC | Highly Competitive (Standard for AI/ML Roles) |
Roles & Responsibilities :
- Model Development: Develop, test, and deploy machine learning models for core product features, including classification, regression, NLP, and Reinforcement Learning (RL).
- Data Pipelines: Build and maintain robust data pipelines for data ingestion, cleaning, feature engineering, and labeling.
- Experimentation & MLOps: Implement reproducible experiments and automate training/evaluation workflows. Contribute to CI/CD for ML services using containers (Docker) and cloud environments.
- Model Monitoring: Monitor live model performance, latency, and data drift. Assist the team in troubleshooting, A/B testing, and incident response for production models.
- Documentation & Review: Participate in code and design reviews. Write clean, well-tested code and create clear documentation and data visualizations for stakeholders.
Education & Skills Required
- Degree: Bachelor’s or Master’s in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
- Target Batch: 2026 and 2025 graduates.
- Experience: 0–2 years of relevant experience (internships, academic ML projects, or research papers absolutely count).
Core Technical Skills :
- Programming: Strong coding proficiency in Python and solid SQL experience for data extraction.
- ML Libraries: Hands-on experience with core machine learning and data manipulation libraries: scikit-learn, pandas, and numpy.
- ML Fundamentals: Deep familiarity with building, tuning, and evaluating machine learning models and understanding core ML metrics (Precision, Recall, F1-score, RMSE).
- Software Engineering: Knowledge of version control (Git), writing unit tests, and basic software engineering practices.
Selection Process :
Kaleris focuses heavily on your mathematical foundation, Python skills, and understanding of ML deployment.
- Resume Screening: Shortlisting based on academic pedigree, GitHub projects, and relevant ML internships.
- Online Assessment: A timed test covering Python programming, SQL queries, Data Structures, and core Machine Learning MCQs (e.g., bias-variance tradeoff, overfitting).
- Technical Interview (ML & Logic): A deep dive into your ML projects. Be prepared to explain the math behind the algorithms you used and write basic Python code for data wrangling on the spot.
- System Design / MLOps Round: High-level discussion on how you would deploy a model into production and monitor it for data drift.
- Managerial & HR Interview: Assessing your cultural fit, passion for supply chain technology, and long-term career goals in AI.
Frequently Asked Questions (FAQs) Kaleris Off Campus 2026 :
1. Is this a Data Scientist role or a Software Developer role? This is a hybrid role (Machine Learning Engineer / MLOps). While you will be developing ML models, a significant portion of your job will involve writing production-ready code, building data pipelines, and deploying these models using cloud infrastructure.
2. Are candidates from non-CS branches eligible? Yes. Kaleris highly values candidates with degrees in Statistics, Mathematics, or core engineering disciplines, provided you have extremely strong Python programming skills and a deep understanding of statistical modeling.
3. Do I need to know cloud computing (AWS/Azure) to apply? While it is not strictly mandatory for a fresher, having basic exposure to Docker, Kubernetes, or cloud deployment will give your resume a massive advantage over other applicants.
How to Apply for Kaleris Off Campus 2026?
Eligible and passionate AI/ML candidates should apply via the official Kaleris/Navis Careers portal. Ensure your resume prominently features links to your GitHub ML projects and any published research papers!
NaukriTech Pro Tip: To crack the Kaleris technical interview, do not just memorize algorithms—make sure you understand the mathematics behind how they optimize (like Gradient Descent) and brush up heavily on pandas for data manipulation! Keep following Nokaritech.com for more premium Data Science and AI job updates.