Senior Software Engineer (Machine learning Platform Engineering )

Positively disrupting an industry that has not seen any innovation in over 50 years Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology big data machine learning and AI to seamlessly bring together OEMs retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3000 people across North America Asia and Europe.

This role offers direct measurable impact on dealer outcomes and consumer experiences acrossTekionsAutomotive Retail Cloud and Automotive Enterprise Cloud with endtoend ownership of an LLM control plane and gateway that serve multitenant workloads under SLAs rich vertical dataset and domain graph spanning sales service parts F&I accounting and consumer touchpoints to power contextaware agents and retrievalaugmented shape core levers - agent orchestration patterns evaluation frameworks and safety guardrails - so improvements in latency reliability evaluation quality and safety translate into dealer KPIs like upsell cycle time CSAT and service enhance the platform to support classical supervised and unsupervised MLmodels .

• Build and run the LLM control plane/gateway: smart routing rate limits/quotas failover and token/cost tracking.

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas structured outputs caching and full observability (traces/logs/metrics).

• Enforce safety and privacy by default: content filtering prompt/response validation and PII redaction.

• Enable multimodel multivendor use LLMs with automated canarying and versioning.

• Own the agent runtime: tool registry permissions function calling grounding and retrieval.

• Design orchestration patterns (sequential plannerexecutor streaming) and manage agent state and longrunning workflows.

• Enabling platform components for training and scoring pipelines for classical ML ( and deep models; standardize experiment tracking and packaging.

• Create components toMonitormodel anddata drift retraining and tuning models as needed tomaintainaccuracy and relevance.

• Add humanintheloop review and safeactioning before agents touch dealer systems.

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines.

• Serve realtime context to agents (profiles inventory pricing appointments service history) with access controls and lineage.

• Power retrieval with hybrid search (graph vector keyword) and smart cache/TTL to balance accuracy latency and cost.

• Run continuous offline/online evaluations for quality factuality bias and safetyfor the platform sanity.

• Define SLOs for latency (p50/p95) uptime andcostviewcapabilities; enable autoscaling and spend controls.

• Maintain a model/agent registry versioning approvals audit trails and reproducibility; support complianceswhere needed.

• Provide templates/CLIs sandboxes and docs so product teams can build and ship fast; mentor engineers and championMLOpsand AI safety best practices.

• 5 years building largescale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design concurrency distributed systems).

• Production experience with Python plus one of Java/Scala/Go; microservices and API design.

• MLOpsat scale: pipelines (Airflow/Kubeflow) tracking/registry (MLflow) CI/CD for models A/B testing shadow/canary and online feature computation (Spark/Flink/Kafka).

• Cloud and containers: AWS (preferred) plus Docker/Kubernetes; performance reliability and cost engineering in multitenant SaaS.

• Practical ML knowledge (feature engineering training evaluation drift detection); experience deploying models that power userfacing workflows.

• Built oroperatedan LLM gateway/control plane: provider adapters routing/policies caching quota/ratelimitcostand token accounting.

• Agentic systems: tool use/function calling orchestration frameworks humanintheloop safety/guardrails and online evaluation/telemetry.

• Graph and retrieval: knowledge graphs (e.g. Neo4j/Neptune/TigerGraph)GraphQL vector search ( hybrid retrieval patterns.

• Platformasproduct: obsess over developer experience paved roads and clearSLAs.

• Thinks in systems-observability fallback access control are core not afterthoughts.

• Passionate about AI-enjoys enabling real-world LLM and agentic use cases.

• Costaware builder: you treat latency and dollars as firstclass metrics and design for graceful degradation.

• Vendoragnostic thinker: choose the right model/provider per use case; build for portability and resilience.

• Documentation and teaching: you make complex systems understandable; you uplevel teams.

Tekion is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability victim of violence or having a family member who is a victim of violence the intersectionality of two or more protected categories or other applicable legally protected characteristics.

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