Senior Manager, AI Reliability Engineering -Kroger Technology & Digital (P2498)

The Senior Manager, AI Reliability Engineering at Kroger Technology & Digital leads the engineering discipline that makes enterprise AI operationally trustworthy at scale.

As the enterprise moves from AI pilots to production systems that associates and customers depend on every day, this leader ensures that models, agents, copilots, AI gateways and shared runtime services are dependable, high-quality, responsive and cost-efficient by design.

This leader will stand up a new discipline from the ground up: defining what production-grade AI means, engineering the standards and automation that bake reliability and quality into every system, and shaping how build teams design for resilience from day one.

The role blends engineering leadership, technical ownership and cross-functional influence. Reliability is a strategic enabler of adoption and velocity - the difference between experimenting with AI and confidently running it at scale.

Key Responsibilities

• Build the Discipline (0-to-1)
• Define what production-grade, operationally trustworthy AI means for the enterprise, including standards and quality bars for availability, behavior, latency, cost, control and recovery.
• Stand up the AI Reliability Engineering function, its charter, operating model, roadmap, talent model and engineering culture.
• Position reliability as an enabler of AI adoption and velocity, creating the confidence that allows the business to scale AI responsibly and aggressively.

• Engineer Reliability and Quality Into Systems
• 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.
• Establish service-level indicators, service-level objectives, error budgets and reliability scorecards that shape architecture, delivery and roadmap decisions.

• Provide Production Readiness and Agent Onboarding
• 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
• Own live observability, production-quality signals and leadership visibility across model and agent behavior, drift, hallucination and quality rates, latency, tool failures and evaluations in production.
• Partner with Responsible AI to translate offline evaluation, risk and safety standards into continuous, automated production signals and operational controls.
• Own the operational capabilities of the Agent Control Center, including estate health, pause, isolation, rollback, shutdown and lifecycle controls for unsupported or persistently unreliable agents.

• Drive Efficiency and Performance
• Engineer for cost and performance at scale, optimizing inference cost, token efficiency, model and routing economics, tool usage and infrastructure consumption as first-class objectives.
• Partner with value tracking, metering, product and finance teams to translate consumption and capacity signals into investment decisions and surface the reliability, quality, latency and cost tradeoffs that shape AI strategy.

Required Qualifications

• Minimum 10+ years of experience in software, platform, cloud, SRE or production engineering, including 3 or more years leading engineering teams.
• Track record of building engineering capabilities, standards, automation or platforms that measurably improved system quality and reliability at scale.
• Strong technical depth in distributed systems, Kubernetes, cloud infrastructure on GCP and/or Azure, networking, identity, automation, CI/CD and modern observability engineering.
• Experience establishing SLOs, error budgets, on-call practices, production-readiness reviews, incident management and post-incident improvement programs.
• Direct experience with ML, AI or LLM systems in production, or demonstrated ability to master failure modes unique to models and agents, including drift, non-determinism, quality regression, prompt and context issues, and hallucination.
• Ability to influence architecture and roadmaps, lead through ambiguity, partner across a matrixed organization and drive outcomes without direct authority.
• Excellent technical and executive communication, with the ability to shape strategy with leaders and go deep with senior engineers.