ML Ops Engineer

A US-based company providing real estate investors with capital to fund deals, loans, and mortgage financing solutions is looking for an ML Ops Engineer. The selected candidate will be responsible for building, testing, and validating ML pipelines that deliver actionable insights. The company is leveraging the power of technology to provide innovative financing solutions that will revolutionize the real estate sector. This is an exciting opportunity for developers who enjoy working in a fast-paced environment while applying their problem-solving and analytical skills. 

Job Responsibilities:

• Build, test, and validate ML pipelines that deliver actionable insights
• Work with various teams to explore different software tools and processes
• Create new products that significantly increase the value of the team 
• Build scalable processes for large-scale data science models
• Develop ML Ops capabilities using open source and commercially available tools
• Follow engineering best practices that reduce the time to publish and improve model tracking in production
• Define and build an experimentation framework to facilitate strategies both for model experimentation and A/B testing
• Work on CI/CD pipelines that will impact the ML models
• Collaborate with data scientists/product owners and deploy ML models from development to production
• Communicate technical and business risks to relevant stakeholders 
• Adapt to changing business needs and new information by pivoting your team to achieve promising initiatives
• Develop strong awareness of the organizational health
• Diagnose, troubleshoot, and make recommendations to solve complex issues on the platform 

 Job Requirements:

• Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
• At least 4+ years of relevant experience as a software engineer
• At least 2+ years of MLOps experience
• Demonstrated ability to ship complex, large-scale ML systems with high availability 
• Extensive experience in developing ML code in Python using various open-source libraries
• Prolific experience with ML systems such as AWS SageMarker, Databricks, etc.
• Experience with design data flow, including ML-specific tasks such as feature generation, feature store, Model Observability, Experimentation, Model serving, etc.
• Nice to have experience with building CI/CD pipelines for ML models
• Prior experience working with Docker and Linux (preferably in Kubernetes) is a plus 
• Experience with SQL and NoSQL databases, specifically related to building ML data pipelines, is preferred 
• Nice to have experience with data pipeline orchestration tools such as Airflow, Dagster, and Prefect
• Experience in agile and test-driven methodologies
• Excellent verbal and written communication skills
• Strong analytical skills with proficiency in answering data-related questions
• Fluent in verbal and written English