Forward Deployed Engineer

Job Description Job Description About zaimler AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it. zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision at scale. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve. zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're growing and deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of enterprise AI runs on, we'd love to talk. About the Job We’re looking for a Forward-Deployed Engineer to join our founding team and bring our AI-native data infrastructure to life in real-world environments. This is a hybrid role blending infrastructure, product, and delivery—you’ll work across systems, tools, and teams to bring new deployments from concept to scale. Think of it as a "jack-of-all-systems" role: one foot in Kubernetes and Ray clusters, the other in semantic pipelines and user-facing use cases. You’ll be a first responder, architect, and operator—deploying zaimler’s semantic infrastructure into dynamic enterprise settings, collaborating closely with ML and infra engineers, and making sure customers get value from day one. This role prefers candidates in or aligned to the Eastern Time Zone (EST) & will require travel onsite to customer locations. What You Will Be Doing 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. Prior Experience 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 Nice to Haves Experience deploying or integrating LLMs or vector databases Background in ML Ops or building/operating data infrastructure at scale Familiarity with GPU optimization, multi-tenancy, or air-gapped environments Prior startup or zero-to-one deployment experience Familiar with Ray Serve, VLLM, or similar frameworks You’ve been in a forward-deployed, solutions engineer or field engineer role before We value builders over résumés. If this role excites you but you don't check every box, we still want to hear from you. zaimler is an equal opportunity employer. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.