ML Software engineer (Python)

IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs. With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.

 

We are looking for a motivated Machine Learning Engineer (Python) to join our fast-moving team. This is a hands-on role ideal for someone with a solid foundation in machine learning, strong Python skills, and a desire to grow in a dynamic and often ambiguous environment. You’ll work closely with product managers, data scientists, and engineers to rapidly prototype, test, and deploy ML-driven features and pipelines.

Responsibilities 

• Collaborate in an agile team to develop and deploy ML models and data pipelines.

• Translate loosely defined business or customer requirements into practical ML solutions.

• Be creative and proactive in shaping solutions that solve real-world problems.

• Build, test, and optimize scalable Python-based systems for training and inference.

• Work with structured and unstructured data (e.g., tabular, text, image).

• Participate in sprint planning, daily stand-ups, and reviews.

• Own and maintain parts of the codebase and contribute to improving engineering best practices.

Collaboration 

Work closely with product, engineering, and customer success teams to understand requirements and deploy ML models

• Communicate technical concepts and progress effectively to both technical and non-technical stakeholders.

• Collaborate on improving team processes, coding standards, and best practices.

• 2–4 years of experience in software engineering or ML engineering roles.

• Strong Python programming skills with experience in ML frameworks like scikit-learn, TensorFlow, or PyTorch.

• Proficiency with data handling libraries (e.g., pandas, NumPy) and tools like Jupyter.

• Ability to take customer or business requirements and creatively turn them into technical solutions.

• Exposure to common ML workflows: data cleaning, feature engineering, model evaluation.

• Comfortable working in a fast-paced, agile environment with changing priorities and some ambiguity.

• Good communication and collaboration skills; you’re proactive and not afraid to ask questions.

• Familiarity with version control (e.g., Git) and containerization (e.g., Docker).

• Experience with cloud platforms (AWS, GCP, or Azure) and basic understanding of MLOps practices.

• Exposure to CI/CD workflows and tools like MLflow or model serving frameworks.

• Ability to build lightweight APIs (e.g., FastAPI, Flask) to expose models as it enables seamless integration of machine learning solutions into production systems