Senior Machine Learning Engineer

Why This Role Matters

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.

What Makes This Opportunity Unique

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also 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 revenue. You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models .

Responsibilities

• 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 multi‑model, multi‑vendor use LLMs with automated canarying and versioning.

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

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows.

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging.

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance.

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems.

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

• Serve real‑time 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 safety for the platform sanity.

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls.

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

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

Desired Skills & Experience

• 5+ years building large‑scale 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.

• MLOps at 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 multi‑tenant SaaS.

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

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting.

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry.

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

Preferred Mindset

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs.

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

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

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation.

• Vendor‑agnostic 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.