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Mid‑level, metro AI platform role with a general senior engineer title increases candidate competition.
Core MLOps and platform engineering skills transfer across industries despite automotive domain specifics.
Explicit 5+ years requirement plus specific MLOps, cloud, and language stack makes filters strict.
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Own and operate the LLM control plane and gateway ensuring smart routing, failover, quotas, cost tracking, and SLA compliance.
Build and maintain real-time AI-native platform components for agentic systems and classical ML workflows impacting automotive dealership operations.
Define and enforce standards for evaluation, deployment, governance, and safety of AI features facilitating fast and safe shipping by product teams.
5+ years experience building large-scale data/ML or platform systems with production software engineering expertise.
Proficient in Python plus one of Java/Scala/Go; experience with microservices, API design, and cloud-native technologies (preferably AWS, Docker, Kubernetes).
Hands-on experience with MLOps pipelines, model tracking/registry, CI/CD for models, and deploying ML models in production.
Experience building or operating an LLM gateway/control plane including provider adapters, routing policies, caching, quota/rate-limiting, and cost/token accounting.
Experienced in designing resilient multi-tenant SaaS platforms with focus on performance, reliability, observability, and cost optimization.
Skilled in architecting agentic AI systems, knowledge graphs, hybrid retrieval (graph+vector+keyword), and human-in-the-loop safety mechanisms.
Developer experience oriented mindset with emphasis on platform-as-product thinking, clear SLAs, and enabling rapid AI feature deployment at scale.