Software Developer 1, Machine Learning

Kinaxis; Chennai District; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

Kinaxis in Chennai, India is seeking a Software Developer for its AI team, focusing on machine learning solutions in supply and demand. The ideal candidate will develop scalable software, analyze complex datasets, and work with modern AI frameworks.

Applicants should have a degree in Computer Science or related fields, with 2-5 years of experience, particularly in Python and cloud technologies. The role emphasizes collaboration, problem-solving, and innovation in developing robust user experiences.

Qualifications

• 2-5 years of relevant experience.
• Proficiency in CI/CD pipelines, preferably with GitHub Actions.
• Experience with debugging and troubleshooting complex issues.

Responsibilities

• Develop large-scale software systems in a fast-paced environment.
• Create data models and analyze complex datasets.
• Ensure software reliability and meet user needs through stakeholder engagement.

Skills

Python

Distributed computing

Kubernetes

Docker

Machine learning

Data analysis

Cloud technologies

REST APIs

Education

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science

Tools

Git/GitHub

Argo Workflows

Azure

AWS

Google Cloud Platform

Job description

About the team

The AI team is responsible for delivering machine learning solutions in the supply and demand space for verticals such as Retail, Consumer Packaged Goods, Life Sciences etc. This includes problems in the space of forecasting, optimization, replenishment, recommendation, explainability, and more. The uniqueness of the team is that it performs at the intersection of technology and real business problems. You will contribute to the product that delights customers world-wide!

What you will do

If you love solving complex problems, analyzing complex datasets, finding insights from data, creating data model and learning new technologies, this role is for you.

• As a software developer, you are passionate about shipping large-scale software systems in a fast-paced environment but can balance longer term issues such as maintainability, scalability, and quality.
• You are fluent in Python, have experience working with distributed computing, big data frameworks and are familiar with Kubernetes and Docker. You have the ability and enthusiasm to learn new technologies whether they are infrastructure or language or platform, and easily adapt to change.
• You have a good understanding of Cloud technologies and Cloud agnostic software architecture and have experience troubleshooting high scale solutions that are deployed and upgraded on a regular cadence.
• You are passionate for software reliability and know how to ensure user needs are met through cross-functional stakeholder understanding and engagement.
• You are excited about finding ways to develop product capabilities and tools that increase robustness of the user experience, reduce the cost of troubleshooting, or reduce the time required to address issues.
• You excel as a team player, a quick starter, and a problem solver. You thrive in cross-functional teams, actively listening and contributing to discussions. Your expertise lies in engineering solutions for complex machine learning challenges, developing Python-based applications, containerizing apps with Docker, orchestrating container swarms in Kubernetes, and building Argo Workflows. These efforts play a key role in creating ML software systems that deliver critical value to the business and its customers.

What we are looking for

This role requires a strong mix of data science expertise, software engineering skills, and problem-solving ability to build scalable, production‑grade solutions.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience.
• 2-5 years of relevant.

Technical Skills

• Agentic & Modern AI Frameworks: Practical experience building solutions using agentic AI patterns, RAG, vector databases, and orchestration frameworks like LangGraph/Google ADK/OpenAI Agent SDK, including prompt engineering and context engineering for production of the agentic workflows.
• AI Driven Software Development: Experience in leveraging agentic software development tools - such as GitHub Copilot, Claude Code or other autonomous agents - to accelerate delivery of new features and resolution of issues.
• Good programming skills in Python.
• Experience with developing REST APIs using frameworks such as Flask or FastAPI.
• Understanding of Docker, Kubernetes, Helm, and Argo Workflows in production environments.
• Understanding of CI/CD pipelines, preferably using GitHub Actions or similar tools.
• Proficiency in version control systems like Git/GitHub.
• Hands‑on experience working with cloud platforms such as Azure, GCP, or AWS.
• Understanding of distributed computing frameworks like Spark, and platforms like Azure Databricks or AWS EMR.

Soft Skills

• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Proven problem‑solving skills and a passion for debugging and troubleshooting complex issues.
• Strong organizational skills with an ability to manage multiple tasks/projects simultaneously.

Nice to Have

• Solid understanding of the end-to-end ML lifecycle data preparation, feature engineering, model training, hyperparameter tuning, model evaluation, and deployment.
• Experience working with structured and unstructured data, large-scale datasets, and optimizing pipelines for performance and scalability.
• Ability to write modular, testable, and maintainable code for ML workflows.
• Domain knowledge in Supply Chain, especially in Demand Planning, CPG, or Manufacturing industries.
• Understanding of how business drivers (e.g., pricing, promotions, seasonality, weather patterns) influence demand.
• Experience working with time series forecasting or predictive modeling in operational contexts.
• Familiarity with MLFlow, DVC, or other ML experiment tracking and versioning tools.
• Exposure to ML model monitoring, or model explainability frameworks.