Role Overview:

This position is for a Software Engineer within the Data Science Portfolio, emphasizing software engineering skills rather than a pure data scientist role. The ideal candidate will possess strong software engineering experience, ideally with exposure to Machine Learning (ML) systems.

Key Responsibilities:

Apply strong backend engineering skills, with a preference for Python and Java.
Develop and build APIs, services, and robust production systems.
Demonstrate solid software engineering fundamentals, including Git/Github Workflows, Unit and Integration Testing, CI/CD, multi-environment deployments, code reviews, and Object-Oriented Programming (OOP) with good class/object design practices.
Work effectively with cloud infrastructure and distributed systems.
Collaborate closely with data scientists, operations research engineers, and product managers.
Take ownership of features end-to-end with minimal oversight.
Exhibit a strong troubleshooting and operational support mindset.

Required Skills:

Languages: Python, Java (Good to Have)
Data Engineering: No SQL Databases (Cosmos), Databricks, Real-time data streaming (Message queues, stream and stateful processing)
MLOps / DevOps: Docker, Kubernetes, CI/CD pipelines, FastAPI / Flask
Cloud & Infra: Azure, Any other cloud platform
SE Fundamentals: Git & version control, Unit & integration testing, Data structures & algorithms
Front End: Good to have React/Angular
Backend: Rest API's

Qualifications:

4-6+ years of experience.

Preferred Skills:

Experience with Data Science, ML, or Operations Research (optimization, MIPs).
Curiosity about machine learning concepts and eagerness to bridge the gap between research and production.

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Software Engineer