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Tekion

Cloud-native automotive retail platform with DMS

Staff Software Engineer - AI Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 8 years of experience building large-scale data, machine learning, or platform systems.
  • Strong software engineering fundamentals, including abstracted API design, concurrency, and distributed systems.
  • Production experience with Python and at least one of Java, Scala, or Go; experience with microservices and API design.
  • Experience with MLOps at scale, including pipelines such as Airflow or Kubeflow, tracking or registries such as MLflow, model CI/CD, A/B testing, shadow or canary deployments, and online feature computation using Spark, Flink, or Kafka.
  • Experience with cloud and containers, including AWS, Docker, and Kubernetes; performance, reliability, and cost engineering in multi-tenant SaaS.
  • Practical machine learning knowledge, including feature engineering, training, evaluation, and drift detection; experience deploying models that power user-facing workflows.
  • Experience building or operating an LLM gateway or control plane, including provider adapters, routing and policies, caching, quotas and rate limits, and cost and token accounting.
  • Experience with agentic systems, including tool use or function calling, orchestration frameworks, human-in-the-loop workflows, safety guardrails, and online evaluation or telemetry.
  • Experience with knowledge graphs such as Neo4j, Neptune, or TigerGraph; GraphQL; vector search such as pgvector, Qdrant, or Milvus; and hybrid retrieval patterns.

Responsibilities

  • Build and operate the LLM control plane and gateway, including smart routing, rate limits and quotas, failover, and token and cost tracking.
  • Ship a unified REST or gRPC API and SDKs with normalized schemas, structured outputs, caching, and observability through traces, logs, and metrics.
  • Enforce safety and privacy by default through content filtering, prompt and response validation, and personally identifiable information redaction.
  • Enable multi-model and multi-vendor LLM use with automated canarying and versioning.
  • Own the agent runtime, including the tool registry, permissions, function calling, grounding, and retrieval.
  • Design sequential, planner-executor, and streaming orchestration patterns; manage agent state and long-running workflows.
  • Enable platform components for classical machine learning training and scoring pipelines, including XGBoost, LightGBM, linear models, trees, and deep models; standardize experiment tracking and packaging.
  • Create components to monitor model and data drift, retrain and tune models, and maintain model accuracy and relevance.
  • Add human-in-the-loop review and safe actioning before agents interact with dealer systems.
  • Evolve the domain graph and entity resolution, and build reliable data-ingestion pipelines.
  • Serve real-time agent context, including profiles, inventory, pricing, appointments, and service history, with access controls and lineage.
  • Power retrieval with hybrid graph, vector, and keyword search, and smart cache and time-to-live controls to balance accuracy, latency, and cost.
  • Run continuous offline and online evaluations for quality, factuality, bias, and safety.
  • Define latency, uptime, and cost-view service-level objectives; enable autoscaling and spend controls.
  • Maintain a model and agent registry, versioning, approvals, audit trails, and reproducibility; support compliance needs.
  • Provide templates, command-line interfaces, sandboxes, and documentation so product teams can build and ship quickly; mentor engineers and champion MLOps and AI safety practices.

Desired Qualifications

  • AWS experience is preferred.
  • Adopt a platform-as-product approach focused on developer experience, paved roads, and clear service-level agreements.
  • Design with observability, fallback, and access control as core system capabilities.
  • Enable real-world large language model and agentic use cases.
  • Treat latency and cost as first-class metrics and design for graceful degradation.
  • Choose models and providers based on use case and build for portability and resilience.
  • Make complex systems understandable through documentation and teaching, and help teams improve their capabilities.

About the Company

Tekion

Cloud-native automotive retail platform with DMS

Tekion provides a cloud-native Automotive Retail Cloud (ARC) that unifies sales, CRM, service, parts, F&I, and accounting for dealers, OEMs, and consumers. ARC runs as a SaaS platform in the cloud on any device and uses AI, machine learning, and big data to enable digital retailing, personalized upsell opportunities, real-time analytics, and automated workflows, removing data silos. Tekion differentiates itself by offering an end-to-end single platform instead of separate tools, backed by unicorn funding and major automotive investors. Its goal is to standardize and modernize automotive retail and become the software backbone for auto retailers worldwide.

Company size
1,001-5,000
Company stage
Growth Equity (Venture Capital)
Total funding
$635.1M
Headquarters
Pleasanton, CA
Founded
2016

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