À propos de ce poste Senior AI Infrastructure Architect chez Huron
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.
Join our team as the expert you are now and create your future.
Responsibilities:
- Build and operate approved model-access patterns for governed AI usage, including Amazon Bedrock where applicable.
- Build infrastructure as code, deployment automation, CI/CD templates, and reusable platform components.
- Implement usage telemetry, token reporting, model usage reporting, user attribution, project attribution, quotas, and cost-management data pipelines.
- Deploy and operate approved sandbox patterns for coding agents and tool-using agents.
- Build governed tool/service mediation patterns for AI applications and agents, including MCP or equivalent approaches where applicable.
- Implement logging, monitoring, alerting, audit evidence capture, and operational reporting for onboarded workloads.
- Build onboarding templates and reference implementations that Client-facing AI Delivery, Enterprise IT, Global Products, analytics, consulting, engineering, and internal operations teams can reuse.
- Use AI tools hands-on to accelerate infrastructure coding, automation, debugging, test creation, documentation, and runbook development.
- Help define runbooks, support models, incident paths, and operational handoffs.
Required Qualifications
- 6+ years of experience building and operating production cloud or platform infrastructure, including hands-on experience with AWS, infrastructure as code, automation, or production operations.
- Strong infrastructure as code experience.
- Experience with cloud networking, IAM, secrets management, logging, monitoring, deployment automation, and operational support.
- Strong software engineering or scripting skills for automation and platform tooling.
- Demonstrated ability to use AI tools as a practical system-building accelerator for infrastructure automation, code generation, debugging, testing, or documentation.
- Familiarity with APIs, event-driven systems, service integration, and secure deployment patterns.
- Ability to work in ambiguous environments and convert platform requirements into working systems.
Preferred Qualifications
- Experience with Amazon Bedrock or other managed AI/model platforms.
- Experience with token/cost telemetry, usage attribution, chargeback, showback, or FinOps data pipelines.
- Experience with Temporal or comparable workflow orchestration platforms for durable infrastructure automation, agent workflows, or operational processes.
- Experience with containerized or sandboxed execution environments.
- Experience with agent tools, MCP, model gateways, API gateways, or secure service broker patterns.
- Experience supporting regulated, client-confidential, PHI, PII, or sensitive workloads.
Flexible living locations across the US. Ability to travel as needed.
The estimated base salary for this job is $175,000 - $245,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $201,000 - $306,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
