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Mid-level ML/MLOps role in metro with known employer and popular skills increases applicant competition moderately.
Role requires deep ML/MLOps and autonomous mapping platform experience, making cross-industry transferability limited.
Explicit 5–8 year requirement plus mandatory MLOps, cloud, Kubernetes, and IaC skills raises filter strictness.
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Build and operate end-to-end Agentic AI/ML pipelines and map compilation platform for high-definition live maps for autonomous driving.
Implement scalable CI/CD/CT pipelines, deploy and monitor ML models in production using cloud-native containerized environments.
Manage AI lifecycle including versioning and rollback; drive Infrastructure as Code practices and platform reliability.
Bachelor’s degree in Computer Science, Engineering, or related field.
5-8 years industry experience with at least 4 years in infrastructure/platform engineering or MLOps.
Strong programming skills in Python, Go, or Bash; hands-on cloud experience (AWS preferred).
Experience with containerization, Kubernetes, Linux fundamentals, distributed systems, and production AI/ML systems.
Experienced engineer bridging ML systems, DevOps, and platform engineering in production environments with autonomous driving data.
Proven track record in operating scalable, fault-tolerant AI/ML pipelines and managing model lifecycle in cloud-native infrastructures.
Familiar working in Agile teams with strong collaboration and proactive adoption of AI-assisted development tools.