Kaleris is hiring an Associate AIML Software Engineer to join its growing AI/ML team in Chennai. This is an excellent opportunity for freshers and early-career professionals to work on real-world machine learning systems that optimize global logistics and supply chain operations.
Detailed Blog (900+ Words)
Introduction
Artificial Intelligence and Machine Learning are transforming industries across the globe, and one of the most impactful areas is logistics and supply chain management. Companies are now leveraging AI to optimize routing, improve efficiency, and enable data-driven decision-making. The role of an Associate AIML Software Engineer at Kaleris offers a strong starting point for individuals who want to build a career in applied AI.
This opportunity is particularly valuable for candidates with 0–2 years of experience who want hands-on exposure to real-world machine learning systems. Working at Kaleris means contributing to mission-critical software that powers global operations while learning modern ML and MLOps practices.
About the Company – Kaleris
Kaleris is a leading provider of cloud-based logistics and supply chain solutions. The company focuses on building software that enhances visibility, efficiency, and reliability across global supply chains.
Kaleris products are used by major operators worldwide, helping them manage yard operations, terminal throughput, and transportation logistics. With a strong emphasis on innovation, the company is building next-generation AI-driven systems that improve decision-making and operational performance.
Kaleris promotes a culture of creativity, inclusion, and continuous learning, making it an ideal environment for early-career engineers.
Role Overview – Associate AIML Software Engineer
The Associate AIML Software Engineer role is designed for individuals at the beginning of their careers who want to work in artificial intelligence and machine learning. The role involves building, testing, and deploying machine learning models that are integrated into real-world products.
You will work closely with experienced engineers and data scientists, gaining exposure to the full machine learning lifecycle—from data preparation to model deployment and monitoring.
Key Responsibilities
1. Machine Learning Model Development
You will develop models for tasks such as classification, regression, natural language processing, and reinforcement learning. This provides exposure to multiple domains within AI.
2. Data Pipeline Development
Data is the backbone of any ML system. You will build pipelines for data ingestion, cleaning, feature engineering, and labeling.
3. Experimentation and Automation
You will design reproducible experiments and help automate training and evaluation workflows, which is critical in production ML systems.
4. MLOps and Deployment
You will contribute to CI/CD pipelines for machine learning services using containerization and cloud technologies.
5. Model Monitoring and Maintenance
Once deployed, models need continuous monitoring. You will track performance, latency, and data drift, ensuring models remain accurate and reliable.
6. Collaboration and Documentation
You will participate in code reviews, design discussions, and create clear documentation for stakeholders.
Required Qualifications
Education:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or related field
Experience:
- 0–2 years (internships, academic projects, or research included)
Skills Extracted from the Job Description and Their Importance
1. Python Programming
Python is the primary language for AI/ML development. Strong knowledge helps in building models, data pipelines, and automation scripts.
2. Machine Learning Libraries (scikit-learn, pandas, numpy)
These libraries are essential for data manipulation, analysis, and model building. They form the foundation of most ML workflows.
3. Understanding of ML Models and Metrics
Knowing how models work and how to evaluate them ensures better performance and reliability in production systems.
4. Data Engineering Skills
Building pipelines for data ingestion and processing is crucial, as poor data quality directly impacts model performance.
5. Version Control (Git)
Collaboration in teams requires proper version control to manage code efficiently.
6. Software Engineering Fundamentals
Unit testing, clean code, and modular design are important for building scalable and maintainable systems.
7. SQL Knowledge
Data extraction and querying are essential for working with structured datasets.
Preferred Skills and Their Value
1. Deep Learning Frameworks (PyTorch, TensorFlow)
These tools are used for advanced AI models, especially in NLP and computer vision.
2. MLOps Tools (MLflow, etc.)
Experiment tracking and lifecycle management improve reproducibility and efficiency.
3. Containerization (Docker, Kubernetes)
These technologies help in deploying scalable and portable ML services.
4. Cloud Platforms (AWS, Azure, GCP)
Modern ML systems are often deployed in the cloud, making this knowledge highly valuable.
5. Reinforcement Learning Interest
This is particularly relevant in logistics optimization, where decision-making systems continuously learn and improve.
Why This Role Matters
The Associate AIML Software Engineer plays a critical role in transforming raw data into actionable insights. At Kaleris, this role directly contributes to improving global supply chain efficiency, reducing costs, and enhancing operational visibility.
By working on real-world AI applications, engineers gain practical experience that goes beyond theoretical knowledge. This makes the role highly valuable for long-term career growth.
Career Growth Opportunities
This role opens multiple career paths, such as:
- Machine Learning Engineer
- Data Scientist
- AI Research Engineer
- MLOps Engineer
- Senior Software Engineer (AI/ML)
With experience, professionals can move into leadership roles or specialize in advanced AI domains.
Work Culture at Kaleris
Kaleris fosters a collaborative and inclusive work environment. Employees are encouraged to innovate, learn, and grow within the organization. The company provides mentorship opportunities, exposure to diverse projects, and a supportive culture for continuous improvement.
Location Advantage – Chennai
Chennai is a major IT and industrial hub in India. Known for its strong engineering talent pool and growing tech ecosystem, Chennai offers excellent opportunities for software professionals.
The city also provides a cost-effective lifestyle compared to other metro cities, making it an attractive destination for fresh graduates.
Conclusion
The Associate AIML Software Engineer role at Kaleris is a strong entry point into the world of artificial intelligence and machine learning. It combines technical learning with real-world application, making it ideal for freshers and early-career professionals.
With hands-on experience in ML development, data engineering, and MLOps, this role prepares candidates for advanced positions in the AI industry. If you are passionate about technology and want to work on impactful global solutions, this opportunity is worth considering.
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