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Moderate competition from a generalist title plus mid-level ML engineering demand.
Role requires specialized MLOps and LLM platform skills but remains transferable across industries with ML platforms.
Multiple mandatory domain skills, tools, and a specific 2+ years requirement raise filtering strictness.
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Own and operate the LLM control plane and gateway enabling multi-provider LLM usage with routing, rate-limiting, cost tracking, and safety enforcement.
Develop and maintain APIs, SDKs, and platform components for agent orchestration, context retrieval, ML model training/scoring pipelines, and continuous evaluation supporting Tekion’s automotive platform.
Define and enforce standards for AI system building, deployment, evaluation, and governance to ensure scalable, safe, cost-efficient delivery of AI-powered dealer features impacting KPIs like upsell, cycle time, CSAT, and service revenue.
Minimum 2 years experience building large-scale data/ML or platform systems with strong software engineering fundamentals.
Proficient in Python plus at least one of Java, Scala, or Go; experience in microservices and API design.
Experience with MLOps tools and pipelines (Airflow/Kubeflow, MLflow), cloud infrastructure (preferably AWS), containerization (Docker/Kubernetes), and multi-tenant SaaS performance optimization.
Practical knowledge of ML deployment, LLM gateway/control plane operation, agentic systems, knowledge graphs, vector search, and hybrid retrieval techniques.
Experienced engineer accustomed to owning complex, scalable AI/ML infrastructure and APIs with a platform-as-product mindset focusing on developer experience and SLA guarantees.
Strong systems thinker emphasizing observability, fallback mechanisms, access control, cost and latency optimization, and vendor-agnostic design for LLM and agentic systems.
Domain knowledge or interest in AI-native automotive retail technology delivering measurable business outcomes through real-time context-aware agentic workflows.