Staff Machine Learning Engineer

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion's DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You'll own pipelines, microservices, CI/CD, observability, and runtime reliability-working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact.

Why this Role Matters

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products.

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders.

• Operationalize secure, compliant, and explainable LLM and agentic services at scale.

• Convert Applied Sciences models into scalable, compliant, costefficient production services.

• Standardize how models are trained, validated, deployed, and monitored across Tekion products.

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference.

What You'll Do

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts.

• Integrate with the LLM Gateway/MCP, prompt/config versioning.

• Build and orchestrate CI/CD pipelines.

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality.

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems.

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems.

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics.

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph.

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets.

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage.

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking.

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation.

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path.

Desired Skills and Experience

• 8 - 11+ years in ML engineering/MLOps or backend/platform engineering with production ML.

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores.

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing.

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.).

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns.

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices.

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering.

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection.

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots.

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience.

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability.

Preferred Mindset

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics.

• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs.

• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency.

• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach.

• Pragmatic: You automate the 80% and leave room for rapid experimentation.