84.51°
Senior Manager, AI Reliability Engineering -Kroger Technology & Digital (P2498)
Cincinnati, OH; · Senior
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About the role
- 84.51° is a retail data science, insights and media company.
- This role is on the Kroger Technology & Digital (KTD) team.
Responsibilities
- Build the Discipline (0-to-1)
- Partner with Platform, Model and Applied AI teams to embed resilience, testability, observability and safe failure modes into AI systems from architecture forward.
- Build reliability tooling and automation, including self-healing, automated evaluations, quality-regression detection, guardrail instrumentation and safe deployment controls.
- Create production-readiness standards covering named business and engineering owners, support models, runbooks, telemetry, quality evaluations, security and Responsible AI controls, escalation paths, service objectives and lifecycle controls.
- Lead launch-readiness reviews for new platforms, models and agents, and make evidence-based readiness decisions with clear exception and risk-acceptance paths.
- Build a scalable onboarding model for both centrally developed agents and domain-owned agents operating on shared enterprise platforms.
- Own Production Quality, Observability and AgentOps
Requirements
- Minimum 10+ years of experience in software, platform, cloud, SRE or production engineering, including 3 or more years leading engineering teams.
- Experience establishing SLOs, error budgets, on-call practices, production-readiness reviews, incident management and post-incident improvement programs.
- Excellent technical and executive communication, with the ability to shape strategy with leaders and go deep with senior engineers.
Nice to have
- Hands-on experience with LLMOps, model observability and evaluation tooling such as LangSmith, MLflow, Arize, Fiddler or custom evaluation pipelines.
- Familiarity with agentic systems, including orchestration, tool use, MCP-based integrations, registries and their unique reliability and quality challenges.
- Background bridging software or SRE practices with ML, data science and Responsible AI engineering cultures, including experience establishing a new engineering discipline.
- Measures of Success:
- SLO attainment, service availability and production quality of AI platforms, models and agents.
- Mean time to detect, contain and recover from incidents.
- Quality-regression detection and containment, change-failure, rollback and repeat-incident rates.
- Reduction in operational toil, noisy alerts and manual interventions.
This listing is sourced directly from 84.51°'s careers page and normalized into a canonical job model.