Applied AI Platform & DevOps Engineer

CohnReznick• , CA

2d•$157,000 - $215,000

About The Position

As CohnReznick grows, so do our career opportunities. As one of the nation’s top professional services firms, CohnReznick creates rewarding careers in advisory, assurance, and tax with team members who value innovation and collaboration in everything they do! CohnReznick helps organizations optimize performance, manage risk, and maximize value through CohnReznick LLP (assurance services) and CohnReznick Advisory LLC (advisory and tax services). Together, the firm provides leaders with deep industry knowledge and relationships, solutions to address clients’ unique business goals and risks, and insight on how emerging market forces can drive opportunity. With offices nationwide, the firm serves organizations around the world as an independent member of Nexia. We currently have an exciting career opportunity for a Applied AI Platform & DevOps Engineer Manager to join the Strategic AI team and lead the development and execution of AI-enhanced business solutions. This role owns the full lifecycle—from problem definition and stakeholder engagement to prototyping, building, evaluating, and preparing solutions for deployment. You will work directly with business owners and product leads to ensure solutions are aligned with strategic goals and integrated into operational workflows. YOUR TEAM. We are seeking an Applied AI Platform & DevOps Engineer to own the end-to-end platform, environments, and operational lifecycle for AI-enabled platforms and applications built by the Strategic AI team. This role is responsible for all environments, CI/CD pipelines, hosting, reliability, availability, security, and operational tooling—with AI embedded natively into the software development lifecycle (SDLC). This is a hands-on engineering role. You will write production code, build and operate shared platforms, and ensure that AI applications developed by the Strategic AI team are production-ready, secure, observable, and scalable. This role does not focus on feature delivery. It owns how AI software is built, deployed, and run. WHY COHNREZNICK? At CohnReznick, we’re united by a common mission to create opportunity, value, and trust for our clients, our people, and our communities. Whether it’s working alongside your peers to solve a client challenge, or volunteering together at the local food bank, there are so many ways to find your “why” at the firm. We believe it’s important to balance work with everyday life – and make time for enjoyment and fun. We invest in a robust Total Rewards package that includes everything from generous PTO, a flexible work environment, expanded parental leave, extensive learning & development, and even paid time off for employees to volunteer.

Requirements

6+ years of experience in DevOps, Platform Engineering, SRE, or Backend Engineering, with ownership of production systems.
Strong hands-on coding experience (e.g., Python, TypeScript/Node.js, Java, or C#).
Deep experience with CI/CD, environment management, and infrastructure automation.
Proven experience owning production hosting, reliability, and availability for distributed systems.
Strong understanding of authentication, authorization, and secure service integration.
Comfort being accountable for operational outcomes in production environments.

Nice To Haves

Experience operating AI-enabled applications in production (LLMs, RAG, agentic workflows).
Familiarity with retrieval systems, embeddings, and vector databases.
Experience implementing observability and runtime controls beyond basic infrastructure metrics.
Experience working in regulated or security-conscious environments.

Responsibilities

Own AI-Native Environments & CI/CD
Design, implement, and operate end-to-end CI/CD pipelines for AI-enabled platforms and applications.
Manage versioning, promotion, and rollback of:
Embed AI-aware testing into CI/CD: Implement safe deployment patterns (canary, shadow, feature flags).
Own Hosting & Runtime for Strategic AI Applications
Own the hosting and execution environment for AI services (APIs, background jobs, agents, workflows).
Design and operate inference orchestration patterns (managed APIs, hybrid or local models as needed).
Implement reliability mechanisms: caching, batching, retries, fallbacks, circuit breakers.
Partner with core infrastructure teams, while retaining ownership of the AI runtime layer.
Reliability, Availability, and Security of AI Systems
Define and enforce SLOs/SLAs for AI platforms and applications.
Build and maintain AI-specific observability, including:
Own security implementation for AI systems: Ensure AI platforms meet enterprise security, privacy, and compliance standards.
AI-Native Software Development Lifecycle
Embed AI capabilities directly into the SDLC: Define standards for how Strategic AI builds, reviews, deploys, and operates AI software.
Continuously improve tooling and workflows to reduce manual effort and operational risk.
Platform & Tooling Development
Build and maintain shared internal tools, libraries, and templates used by the Strategic AI team.
Create “golden paths” for common AI patterns (e.g., RAG, agent workflows, orchestration).
Reduce friction for Applied AI Engineers by providing reliable, well-documented infrastructure and tooling.
Future / Optional Scope (Secondary Priority)
As capacity allows, contribute to processes or tooling that help transition experimental or business-built AI prototypes into managed Strategic AI platforms. This is not a primary responsibility and does not detract from core platform ownership.