4 months ago
Bengaluru, IndiaStaff+
Responsibilities
- Develop a deep understanding of the AI Platform architecture, services, data and feature flows, and supported ML lifecycle.
- Own end-to-end quality for feature pipelines, training and serving infrastructure, model registries, deployment pipelines, LLM gateways, and vector or data stores.
- Design and implement automated testing frameworks for platform APIs, SDKs, services, and infrastructure components.
- Validate feature and model input/output correctness, completeness, and consistency across platform flows.
- Test model-serving and inference services for correctness, latency, throughput, scalability, and reliability under load.
- Build regression and reconciliation suites for platform changes, migrations, and version upgrades.
- Validate ingestion, feature computation, model deployment and rollback, multi-tenancy, and governance capabilities.
- Define quality metrics and SLO validation with automated monitoring for platform anomalies.
- Contribute to evaluation frameworks for platform-level AI services.
- Use AI and LLMs to generate tests, synthetic data, and automation scaffolding.
- Lead root cause analysis for production platform issues and promote quality and reliability practices.
Requirements
- 8+ years of experience in SDET, quality engineering, or software engineering.
- Strong programming skills in Python and/or Java/Go and the ability to write production-quality automation and tooling.
- Experience testing distributed systems, APIs, data pipelines, or ML and data platforms.
- Understanding of ML lifecycle components such as feature stores, training and serving, model registries, and deployment.
- Experience with performance, load, and scalability testing.
- Familiarity with CI/CD, infrastructure-as-code, Docker, Kubernetes, and AWS, GCP, or Azure.
- Understanding of LLM/ML failure modes and evaluation concepts.
- Strong debugging and root cause analysis skills across service, data, and infrastructure layers.
- Excellent collaboration and communication skills.
- Nice-to-have experience with ML platform tools such as MLflow, Feast, KServe/Seldon, Ray, Kubeflow, SageMaker/Vertex AI, or LLM infrastructure such as vLLM, model gateways, and vector databases including Milvus or Pinecone.
- Nice-to-have experience with Prometheus, Grafana, Datadog, multi-tenant high-throughput SaaS platforms, internal test frameworks, developer tooling, chaos engineering, or reliability engineering.
Tech Stack
Categories
About Tekion
Tekion builds an AI-native, cloud platform for automotive retail that unifies dealers, OEMs, and partners. Its products include Automotive Retail Cloud (a dealership management system for retailers), Automotive Enterprise Cloud for manufacturers, and Automotive Partner Cloud for integrations, delivered as subscription software. Privately held and headquartered in Pleasanton, California, Tekion raised private equity funding in 2024.