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DataRobot is looking for a Data Science Software Engineer to build production-ready AI solutions that help organizations around the globe adopt AI/ML at scale. In this role, candidates will develop backend infrastructure, work with data science teams, and implement AI/ML solutions that are resilient, scalable, and maintainable.
Location: Remote (India)
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Build and maintain reliable and scalable, production quality backend solutions that serve the AI/ML pipelines of data scientists.
Construct architecture, reusable components, for rapid integration of AI capabilities into SAP and other enterprise solutions.
Create a data pipeline to connect multiple data sources to ML models to develop analytic and AI solutions.
Utilize programming tools and technologies to build AI solutions that are resilient and maintainable while being easy to deploy with as little operational friction as possible.
Collaborate with data scientists in order to determine their infrastructure-related needs and help them to translate those needs to recommended technical solutions.
Work on engineering problems during high-intensity periods on projects and also contribute to innovation during low-intense periods.
Apply, track, and enhance engineering best practices to achieve better performance, scalability, and maintainability of AI/ML solutions.
Assist and troubleshoot production systems in order to maintain reliability and performance.
Create and implement a CI/CD pipeline to capture the automated testing, build, and deployment process of ML models.
Containerize applications and use infrastructure as code to ensure the environment is constant.
Monitor system performance and establish a logging strategy for ML applications.
Over four years of experience with Python for backend services or data science workflows.
A strong ability to write efficient, maintainable, and well-structured code regarding reusability and scalability.
Experience developing APIs preferably using FastAPI, Flask, or like libraries or frameworks.
Experience with containerized technologies (Docker) and orchestration (Kubernetes).
Experience with Infrastructure as Code tools (Terraform, Cloudformation, Pulumi).
Experience building CI/CD pipelining (CI) tools (Jenkins, GitHub Actions, GitLab CI).
Experience with principles of data engineering (ETL, Health Data Pipelines).
Ability to work within an environment of varying workloads, and capable and willing to do focused project sprints, and more exploratory work.
Highly coachable and eager to learn. Strong problem-solving ability. Can adapt to changing goals or direction.
Experience with cloud-based AI/ML infrastructure (AWS, Azure, GCP).
Familiarity with SAP integration technologies or enterprise system integrations.
Baseline knowledge of machine learning operations (MLOps) practices.
Understanding how to version control data and models.
Exposure to generative AI solutions, such as RAG, and fine-tuning, etc.
DataRobot powers the future of enterprise AI, allowing organizations to transform data into real-time actionable insights. DataRobot’s platform combines automation of machine learning and generative AI workflows, enabling organizations to make smarter decisions, reduce risk, and provide actual, real-world impact. DataRobot enables AI to be applied in practice at scale and is accessible to all teams and industries.
