DevOps / GenAIOps
Role Purpose
The DevOps AI Manager leads the cloud infrastructure, delivery automation, and operational practices that support AI-enabled products and their surrounding software systems. The role reduces delivery bottlenecks by owning infrastructure and release work end to end, while learning the product architecture deeply enough to review changes for operational and architectural risk. It focuses on production readiness across the full system, including the substantial non-model software, data, APIs, and services around AI capabilities.
Scope and Autonomy
Owns DevOps and platform outcomes for one or more AI product environments, from architecture and infrastructure as code through CI/CD, observability, incident readiness, and cost and performance improvement. Works closely with software architecture and engineering leads. Operates with high agency, turns incomplete requests into a proposed plan, and brings focused decisions to stakeholders. The title denotes functional technical leadership; people management is included only when explicitly assigned.
Role Boundaries
This role owns platform and operational engineering decisions within approved project and client constraints. It does not independently change product behavior, approve security exceptions, alter data governance, commit unapproved spend, or accept material business risk. The role may identify architectural defects in code and pipelines and recommend or block a deployment under agreed quality gates; application architecture authority remains shared with the designated Software Architect and accountable product owner.
Core Responsibilities
AI Product Platform and Infrastructure
Design, provision, and operate secure, repeatable cloud environments for AI-enabled products and their supporting APIs, databases, queues, background jobs, and distributed services.
Translate product and architecture requirements into infrastructure designs, capacity assumptions, network and identity controls, resilience patterns, and operational runbooks.
Use infrastructure as code and configuration management to make environments consistent, reviewable, recoverable, and suitable for development, testing, staging, and production.
Delivery Engineering and Release Management
Build and improve CI/CD pipelines, deployment automation, release controls, environment promotion, rollback, database migration, and recovery procedures.
Partner with engineering leads to make releases observable and low risk, including staged rollout, deployment verification, and clear ownership during production changes.
Review application pull requests and release plans for operational risks such as resource limits, concurrency, service dependencies, data migration, latency, and failure handling.
Reliability, Security, and Performance
Establish actionable monitoring, logging, tracing, alerting, service health indicators, and incident response practices across cloud infrastructure and application services.
Lead or support incident investigation, root-cause analysis, recovery, and follow-up improvements; maintain useful operational documentation and escalation paths.
Improve availability, latency, scalability, recoverability, and cloud cost using measured system behavior and agreed service objectives.
Apply least privilege, secret management, secure networking, vulnerability management, backup validation, and relevant client security controls.
Technical Leadership and Enablement
Learn the AI product codebase and system interactions sufficiently to understand how agents, APIs, voice or other product services, data stores, and infrastructure affect operations.
Coordinate infrastructure work across DevOps, software engineering, architecture, and product; reduce dependence on ad hoc direction by identifying and sequencing work proactively.
Mentor engineers and document repeatable practices. Build operational redundancy and shared understanding so critical platform knowledge is not held by one person.
Evaluate cloud and platform tools against actual product needs, security obligations, reliability, maintainability, and total cost.
Accountability and Success Measures
Assess results over time using production evidence, delivery outcomes, system risk, and team feedback. Infrastructure ticket volume, deployment frequency, uptime, or cloud savings alone should not determine performance.
Accountability | Evidence of effective performance |
Infrastructure ownership | Environments and core services are documented, reproducible, secure, supportable, and aligned to product architecture. |
Release safety | Deployments have clear validation, migration, rollback, and recovery steps; avoidable release incidents decrease. |
Operational visibility | Teams can detect and diagnose service degradation through useful logs, metrics, traces, alerts, and runbooks. |
Reliability and performance | Latency, capacity, availability, and recovery risks are measured and improved against agreed product needs. |
Team resilience | Critical platform knowledge, access, and operational practices are shared, reducing single-person dependency. |
Edify Competencies
Competency | Expected behavior |
Connection | Builds trust through clear communication, dependable follow-through, and constructive collaboration with engineering, product, operations, and client stakeholders. |
Vision | Connects technical choices to user outcomes, system risk, delivery goals, operational constraints, and long-term maintainability. |
Empowerment | Creates shared context, clear interfaces, useful documentation, and coaching that enable teams to own their work. |
Initiative | Anticipates gaps, acts on ambiguous problems, and escalates decisions when impact exceeds role authority. |
Position Requirements
Education and Experience
Technical degree or equivalent demonstrated capability in Computer Science, Software Engineering, Cloud Engineering, Information Systems, or a related field.
Substantial experience operating production cloud platforms and delivery systems. Evidence of production ownership, sound judgment, and ability to work through a complex codebase is more important than a fixed number of years.
Technical and conversational English at B2+ or higher under the Common European Framework of Reference.
Required Capability and Tools
Advanced practical experience with at least one major cloud platform (AWS, Azure, or GCP), Linux/Unix, networking fundamentals, identity and access management, and cloud security.
Strong infrastructure as code skills using Terraform or an equivalent tool; scripting or programming capability in Python, Bash, or a comparable language.
Experience designing CI/CD and release automation, containers and orchestration, environment configuration, secrets management, database migration, backup, and recovery.
Production experience with distributed services, APIs, asynchronous processing, queues, concurrency, and application operations; able to review code and reason about failure modes, dependencies, and resource usage.
Hands-on observability and incident response experience using appropriate tools for metrics, logs, traces, alerting, and service health.
Ability to investigate latency, reliability, and scaling issues and to communicate tradeoffs, evidence, and operational risk.
Useful Additional Experience
Experience operating systems that include AI agents, model APIs, retrieval pipelines, evaluation jobs, or voice and real-time services.
Experience with Kubernetes, GitHub Actions, GitLab CI, Azure DevOps, Prometheus, Grafana, CloudWatch, ELK, or comparable tools.
Experience with SOC 2 or other security audits, production readiness reviews, disaster recovery exercises, or client-facing technical leadership.
Edify is proud to be an equal-opportunity employer committed to diversity and inclusion. We encourage all candidates to apply regardless of their sex, gender identity, sexual orientation, race, color, religion, national identity, disability, age, medical condition, pregnancy, marital status, family status or any other characteristic protected by law.