Senior Infrastructure AI Specialist
Core
Define AI/GenAI strategy and lead design/delivery of AI capabilities embedded in infrastructure platforms, including MLOps/GenAIOps frameworks and observability.
Role type
Senior IC infrastructure AI specialist (MLOps/GenAIOps)
Builds
Production-grade MLOps/GenAIOps platforms, scalable AI pipelines, RAG systems, and agentic AI workflows for infrastructure automation.
Domain
Cloud infrastructure, AI/ML operations, enterprise IT services
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, AWS SageMaker, Azure ML Studio, Google Vertex AI, LLM architectures, RAG, fine-tuning (LoRA, QLoRA), agentic AI frameworks (LangChain, AutoGen, LlamaIndex, CrewAI), vector databases, Kubernetes for AI, ITIL processes, ServiceNow integration, AI security, Infrastructure as Code (Terraform, Ansible), AI governance (EU AI Act)
Preferred skills
Multimodal AI, CUDA/Triton optimization, AI-native observability platforms (Dynatrace, Datadog), open-source AI contributions, edge AI/IoT
Technologies
AWS, Azure, GCP, Kubernetes, Docker, Terraform, Ansible, LangChain, AutoGen, Pinecone, Weaviate, Chroma, pgvector, ServiceNow, Dynatrace, Datadog
Responsibilities
Architect scalable AI pipelines for model training, evaluation, deployment, and lifecycle governance; Lead implementation of GenAI-powered infrastructure use cases (incident prediction, capacity planning, anomaly detection, self-healing); Oversee GPU/TPU compute infrastructure provisioning and optimization; Implement AI governance frameworks (model explainability, bias auditing, regulatory compliance); Define MLOps tooling strategy (feature stores, experiment tracking, model registries, inference serving); Optimize AI FinOps and TokenOps practices; Ensure AI security and secure integration with enterprise platforms; Mentor junior AI specialists; Contribute to business development by shaping AI-enabled infrastructure propositions.
Seniority
Senior, hands-on IC with mentorship responsibilities