Implementation Engineer collaborating with clients to integrate renewable energy assets onboard a SaaS platform. Partnering with internal teams to ensure seamless data integration and system configuration.
About the role
- Forward-Deployed Engineer with zaimler working on AI-native data infrastructure in dynamic customer settings. A hybrid role involving infrastructure, product, and delivery with a focus on Kubernetes and semantic pipelines.
Responsibilities
- Build, deploy, and scale zaimler’s platform in diverse customer environments (Kubernetes-native, often hybrid cloud)
- Own the infrastructure stack: provisioning, scaling, monitoring, and incident handling for data/ML pipelines.
- Stand up and optimize distributed compute systems using Ray, Kubernetes, and supporting cloud services.
- Bridge ML and infra. Help ML engineers productionize knowledge extraction, entity linking, and retrieval pipelines
- Write Terraform, Helm charts, Dockerfiles, and bash scripts that others rely on
- Contribute to internal tooling, automation, and observability to make zaimler repeatable and scalable.
- Interface directly with early customers to understand their environment, workflows, and edge cases.
Requirements
- 3+ years of experience in backend or infrastructure roles (infra/platform, SRE, or devops welcome)
- Deep familiarity with Kubernetes, Docker, Terraform, and Helm
- Experience managing or deploying distributed compute frameworks (Ray, Dask, Spark, etc.)
- Fluency in at least one scripting language (Python, Bash, Go preferred)
- Strong debugging skills across the stack—from network hiccups to memory leaks
- Experience deploying in cloud-native environments (AWS/GCP/Azure)
- Comfortable being customer-facing and managing ambiguity in real-time
- Strong sense of ownership and bias toward action in early-stage environments
Job title
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Degree requirement
Tech skills
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