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Job Description
Structured overview of role & requirementsAbout This Role
Own and maintain model monitoring and observability systems including drift detection, performance tracking, and automated alerting using tools like Arize.
Lead data quality assurance through continuous validation of data pipelines to ensure data integrity before model scoring.
Build and operate scalable cloud-based CI/CD pipelines and infrastructure as code for deploying and updating ML models and monitoring solutions.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
At least 5 years of software development experience (Bachelor's degree) or 3 years (advanced degree) with ML, GenAI, or full-stack development focus.
Minimum 3 years collaborating with data science and engineering teams on ML system design and scaling, plus 4+ years hands-on with public cloud platforms (GCP, AWS, Azure).
Experience with Terraform or similar IaC tools for ML pipelines, CI/CD and MLOps practices for at least 3 years, and experience in agile development environments including SAFe.
Ideal Candidate Profile
Experienced in deploying and operating scalable, reliable ML production systems integrating observability and monitoring.
Proficient in working cross-functionally with data scientists, engineers, and business stakeholders to deliver AI/ML solutions aligned to business outcomes.
Balanced skillset in software development, cloud engineering, and ML infrastructure automation with a strategic focus on quality assurance and model lifecycle management.