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AI Infrastructure Specialist
2 weeks ago
Job Description:
As a leader in AI infrastructure, we are seeking an expert to design, implement, and scale our machine learning systems. This individual will partner with researchers, product teams, and DevOps to turn prototypes into production services that meet strict performance standards.
- Develop scalable ML pipelines for large language models, computer vision, and multimodal models.
- Lead efforts in model serving, versioning, automated continuous integration/continuous deployment, and real-time monitoring of AI workflows.
- Build and optimize GPU-backed serving infrastructure targeting sub-100ms latency, 99.9% uptime, and >80% GPU utilization.
- Drive initiatives on model governance, automated drift detection (80% utilization.
- Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals.
- Mentor junior engineers, run quarterly brown-bag sessions, own onboarding documentation (upskill 5+ engineers/quarter), and publish ≥1 open-source contribution or talk annually.
Requirements:
- 9–14 years in software engineering, including ≥4 years in MLOps or ML infrastructure
- Strong expertise in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow
- Experience with inference frameworks (Triton, TensorFlow Serving, BentoML, TorchServe)
- Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration
- Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.)
- Proven track record building reliable, scalable ML systems in production.
Critical Skills:
- Vector DB integration (Qdrant, Pinecone)
- Agent orchestration (LangChain, LlamaIndex)
- Multi-tenant security and RBAC
- Observability stacks (Prometheus/Grafana, ELK)
- CI/CD for FastAPI/NestJS services
Preferred:
- Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD.
- Prior experience at AI-focused startups or enterprises scaling ML for multiple organizations.
- Understanding of low-latency streaming inference or agent-based LLM systems.
- Excellent written and verbal communication, and a proven ability to drive consensus across functions.