Senior Inference Service SRE and Automation Engineer
Bitdeer is a technology company providing Bitcoin mining solutions.
Maintainer signals as of 9/25/2026
Funding history
Investors
Projects
About Bitdeer
Bitdeer provides full-spectrum Bitcoin mining and high-performance computing solutions, including SEALMINER mining equipment, Minerbase cooling containers, cloud mining, co-mining, mining management applications, mining rights marketplaces, and large-scale data center operations. The company also offers AI cloud infrastructure with GPU computing, model training and deployment capabilities, and turnkey AI data center solutions for enterprise customers and developers. Bitdeer is headquartered in Singapore and operates globally.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own end-to-end reliability for customer inference endpoints, from API gateway to GPU model replicas and shared control-plane dependencies. You will define SLOs, manage capacity and incidents, automate remediation, run failover and resilience drills, improve observability, and protect production changes through safe rollout practices.
Requirements
- Have 5 or more years of SRE or production platform operations experience
- Have 2 or more years supporting a customer-facing API with a published SLA
- Have deep Kubernetes operations experience
- Have production experience serving LLM or model inference with vLLM, SGLang, TensorRT-LLM, Triton, or equivalent
- Have GPU fleet operations experience including DCGM, Xid, ECC, NVLink, and NCCL diagnosis
- Know traffic engineering, load balancing, gateways, health checks, DNS/GSLB, and failover
- Know SLI, SLO, SLA, error budgets, incident command, postmortems, and capacity planning
- Have experience operating PostgreSQL, Redis, message queues, object storage, and secrets management
- Know Terraform, Helm, GitOps, ArgoCD, or Flux
- Program in Go or Python for automation and operator development
Responsibilities
- Own availability, latency, request success rate, and SLA delivery for inference endpoints
- Operate the API gateway, inference router, scheduler, queue, serving path, and KV cache service
- Manage inference runtime health, model validation, rollout, and rollback
- Design model-replica topology and failure isolation
- Manage multi-cluster traffic steering, failover, and regional recovery
- Monitor GPU fleet health and automate draining, cordoning, and replica replacement
- Operate Kubernetes and shared control-plane dependencies
- Engineer capacity, admission control, load shedding, and graceful degradation
- Define and instrument SLIs, SLOs, SLAs, error budgets, and alerting
- Maintain observability and external synthetic inference probes
- Lead incident management, customer communication, postmortems, and follow-up actions
- Implement staged rollout, canary deployment, and automatic rollback
- Run failover drills, disaster recovery tests, game days, and backup-restore verification
- Convert manual interventions into automated workflows
