Software Developer 1, Machine Learning

About Kinaxis

Are you looking to join an innovative, market-leading company where you can truly elevate your career? At Kinaxis we are serious about culture, we are serious about technology, we are serious about customers, and we are serious about not taking ourselves too seriously. If you are looking to be part of an incredible growth story, then we might just be the place for you!

In 1984, we started out as a team of three engineers. Today, we have grown to become a global organization with over 2000 employees around the world, 6 global office and a best-in-class HQ in Ottawa, Canada. As winners of several Top Employer awards globally, we are proud to work with our customers and employees towards solving some of the biggest challenges facing supply chains today.

Kinaxis is a global leader in modern supply chain orchestration, powering complex global supply chains, and supporting the people who manage them. Our powerful, AI infused platform provides full transparency and visibility across end-to-end supply chains, enabling our customers to make faster, better decisions. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption. With more than 40,000 users in over 100 countries, we are expanding our team as we continue to innovate and revolutionize how we support our customers.

Location

Bangalore, India

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.

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