Senior Enterprise AI Architect: Scalable AI Engineering

OP

United States

On-site

USD 150,000 - 230,000

Full time

5 days ago

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Benefits offered by this job

401(k)

Dental Insurance

Health insurance

Vision insurance

Job summary

OrangePeople is seeking an experienced AI Architect to move clients from basic AI experiments to a governed, scalable AI engineering capability. You will design AI‑powered engineering frameworks, build reusable agent capabilities, and automate SDLC workflows across engineering, architecture, QA, DevOps, security, product, and leadership teams.

You will lead adoption initiatives, evaluate platforms and agent frameworks, and define secure AI practices with human oversight.

Qualifications

8 to 10+ years of experience in software engineering, enterprise architecture, platform engineering, DevOps, or technology transformation.
Hands‑on experience with Generative AI, AI engineering, coding assistants, copilots, or agentic AI solutions in enterprise environments.
Strong understanding of software architecture, APIs, integrations, cloud platforms, DevOps, DevSecOps, CI/CD, testing, release management, security, and data governance.

Responsibilities

Design enterprise AI engineering frameworks, reference architectures, operating models, and adoption roadmaps.
Define AI‑assisted SDLC methodologies covering requirements, planning, architecture, development, testing, code review, documentation, release, and operations.
Architect single‑agent and multi‑agent workflows with human‑in‑the‑loop controls, approval gates, auditability, and safeguards.
Build reusable AI skills, copilots, plugins, tools, prompts, agents, playbooks, and workflow templates.
Integrate AI capabilities with source control, work management, CI/CD, testing, security scanning, documentation, collaboration, and observability tools.
Establish governance for AI usage, model selection, prompts, data access, privacy, security, logging, monitoring, cost controls, and Responsible AI practices.
Define secure AI adoption practices including data classification, least privilege, sandboxing, prompt hygiene, context hygiene, and protection against risks such as hallucination, excessive agency, prompt injection, data leakage, and unsafe tool access.
Create AI‑enabled automation for backlog analysis, implementation planning, impact assessment, code generation, test creation, review automation, release readiness, documentation updates, and closed‑loop remediation.
Evaluate AI platforms, coding assistants, model ecosystems, and agent frameworks based on security, privacy, functionality, extensibility, integration, scalability, cost, auditability, and enterprise fit.
Lead discovery sessions, pilots, proofs‑of‑concept, workshops, architecture reviews, enablement programs, and scaled adoption initiatives.
Communicate AI strategy, technical decisions, risks, tradeoffs, and business value to engineering teams and executive stakeholders.