Staff AI Platform Engineer
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Job Description
Staff AI Platform Engineer owning gateways, agent orchestration, governance, and observability. Building AI infrastructure for Technosylva’s wildfire and natural-hazard management software.
Responsibilities:
- Own the AI Platform architecture and set its technical vision and standards
- Document the architecture
- Lead the evolution of the AI gateway and model-routing decisions, including provider routing, hybrid and on-prem routing, failover, caching, rate limits and multi-provider compatibility
- Decide the agent orchestration stack
- Design sandboxing and lifecycle management for agents that run code or access internal systems
- Define multi-agent and human-in-the-loop patterns
- Design identity and access control for people and non-human agent identities
- Lead model, tool and MCP allowlists and access tiers
- Work with Legal and Security on EU AI Act and data-classification requirements
- Shape the skills/plugins repository and define the security model for the curated MCP registry
- Standardize tooling for tracing agent decisions, evaluations, drift detection and quality-regression monitoring
- Design cost attribution and allocation for token and compute spend
- Serve as technical escalation point for embedded AI delivery
- Unblock teams, review designs and create golden paths
- Partner with Security teams on threat modelling and controls for prompt injection, data exfiltration and over-privileged tools
- Mentor engineers, run design reviews and RFCs, and represent AI Platform in engineering-wide technical decisions
- Participate in on-call rotation and incident response for production AI services
Requirements:
- 8+ years in software, platform, infrastructure or SRE engineering
- Experience at Staff/Principal level or in a tech-lead role owning architecture across teams
- Strong understanding of LLM and API fundamentals, including context windows, tool use, structured outputs, streaming, prompt caching, and provider API differences
- Hands-on experience designing or running an LLM gateway or similar layer, including build-vs-buy decisions
- Experience with agent orchestration patterns, including multi-agent workflows, delegation, human-in-the-loop approval gates, memory and context management, and token optimization
- Experience working with Security teams on threat modelling, security reviews and risk acceptance for production systems
- Working knowledge of the Model Context Protocol (MCP), including implementing servers, scoping tools, and reviewing third-party servers for security
- Production experience with containers and isolation, including Docker and Kubernetes
- Infrastructure as code experience with Terraform or similar on at least one major cloud; Azure preferred
- Solid grasp of observability, including OpenTelemetry, Prometheus and Grafana, and reliability engineering for non-deterministic AI systems
- Strong coding skills
- Record of influencing without authority, writing RFCs and architecture decision records, gaining team agreement, and explaining technical trade-offs to non-technical stakeholders
- Nice to have: experience with AI evaluation and observability tools
- Nice to have: FinOps experience with usage-based cost models
- Nice to have: familiarity with AI governance frameworks including EU AI Act, NIST AI RMF and ISO/IEC 42001
- Nice to have: experience with AI coding agents and extension models
- Nice to have: hands-on experience designing or evaluating model routing solutions
- Nice to have: experience serving self-hosted or on-prem models
- Nice to have: experience building developer platforms, golden paths or internal developer portals
Benefits:
- Competitive annual salary
- Private health insurance
- Flexible benefits plan allowing employees to tailor part of their compensation package to personal needs
- Annual bonus based on individual performance and company results
- Flexible working hours
- Remote work options
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