Инженер DevOps

Ümumi məlumat

We are looking for an AI Platforms Engineer to design, build, and operate the foundational platform that enables teams to develop, deploy, govern, and scale AI solutions reliably and securely. This role sits at the intersection of platform engineering, MLOps, infrastructure, data, and developer enablement.

Öhdəliklər

• Design and build scalable AI platform capabilities for training, fine-tuning, inference, evaluation, and experimentation.
• Develop and operate shared platform services for: model serving,vector databases,feature/data access,prompt and agent workflows, GPU workload orchestration,secrets and configuration management.
• Build reusable MLOps/LLMOps pipelines for model packaging, deployment, rollback, versioning, and lifecycle management.
• Enable secure deployment and operation of: open-source models, commercial model APIs, retrieval-augmented generation systems, agent-based workloads.
• Create internal self-service tooling, templates for AI application teams.
• Implement platform controls for: authentication and authorization, rate limiting and quota management, audit logging, data protection, policy enforcement, guardrails.
• Build observability for AI workloads, including: latency,throughput,token usage,GPU utilization,model/system health,drift and quality indicators.
• Improve reliability and efficiency of AI infrastructure through automation, SRE practices, and performance tuning.
• Partner with data scientists, software engineers, architects, security teams, and business stakeholders to translate AI use cases into robust platform capabilities.
• Define standards and best practices for AI platform architecture, CI/CD, monitoring, governance, and operations.
• Support evaluation and integration of emerging AI infrastructure technologies, frameworks, and tools.
  
  

Tələblər

• Strong experience with Kubernetes and containerized workloads in production
• 5+ years experience in production infrastructure as a Platform, SRE, DevOps, or MLOps engineer
• Strong scripting and automation in Go and/or Python — enough to write a Kubernetes controller, or a non-trivial operational tool
• Experience with CI/CD pipelines, Infrastructure-as-code and GitOps: Gitlab CI, Terraform, Ansible, Helm, ArgoCD or Flux
• Solid understanding of Linux, networking, storage, and security
• Experience with monitoring and observability tools such as Prometheus, Grafana, OpenTelemetry
• Understanding of the ML lifecycle: training, deployment, inference, evaluation, and monitoring
• Experience building or operating shared platforms used by multiple teams
• Ability to work closely with data scientists, ML engineers, software engineers, and security teams
• Depth in at least one of: GPU infrastructure and inference ops (vLLM, NVIDIA GPU operator, MIG, quantization, inference performance tuning); platform SRE at scale (multi-tenant Kubernetes, 99.9%+ SLOs, capacity planning); MLOps (model registry, deployment pipelines, canary and shadow rollouts, evaluation, Langfuse or equivalent LLM observability); or API gateway operations (Kong, Envoy, or Istio at production scale, plugin development, request-path performance tuning)
  
  

Nice To Have

• Experience with LLM / GenAI platforms
• Experience with model serving tools such as vLLM, Triton, TGI, Ray, KServe, or TorchServe
• Familiarity with RAG, embeddings, reranking, and vector databases
• Experience with GPU infrastructure and scheduling AI workloads
• Experience integrating open-source models and commercial AI APIs
• Experience with identity and access management such as LDAP, SAML, OIDC, OAuth2