Data Science - Senior Engineer

Posted On: 8 Jun 2026 Location: Noida, UP, India Company: Iris Software Why Join Iris? Are you ready to do the best work of your career at one of India’s Top 25 Best Workplaces in IT industry? Do you want to grow in an award-winning culture that truly values your talent and ambitions? Join Iris Software — one of the fastest-growing IT services companies — where you own and shape your success story.

At Iris Software, our vision is to be our client’s most trusted technology partner, and the first choice for the industry’s top professionals to realize their full potential. With over 4,300 associates across India, U.S.A, and Canada, we help our enterprise clients thrive with technology-enabled transformation across financial services, healthcare, transportation & logistics, and professional services. Our work covers complex, mission-critical applications with the latest technologies, such as high-value complex Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation. Working with Us At Iris, every role is more than a job — it’s a launchpad for growth. Our Employee Value Proposition, “Build Your Future. Own Your Journey.” reflects our belief that people thrive when they have ownership of their career and the right opportunities to shape it. We foster a culture where your potential is valued, your voice matters, and your work creates real impact. With cutting-edge projects, personalized career development, continuous learning and mentorship, we support you to grow and become your best — both personally and professionally. Curious what it’s like to work at Iris? Head to this video for an inside look at the people, the passion, and the possibilities. Watch it here.

Key Responsibilities

: • Design, implement, and maintain data science pipelines from data ingestion to model deployment. • Collaborate with data scientists to operationalize ML models and algorithms in production environments. • Develop robust APIs and services for ML model inference and integration. • Build and optimize large-scale data processing systems using Spark, Pandas, or similar tools. • Ensure data quality and pipeline reliability through rigorous testing, validation, and monitoring. • Work with cloud infrastructure (AWS) for scalable ML deployment. • Manage model versioning, feature engineering workflows, and experiment tracking. • Optimize performance of models and pipelines for latency, cost, and throughput.