Systems Engineer

OrangePeople is looking for an experienced and forward-thinking AI Architect to help customers move from basic AI experimentation to a governed, scalable, and measurable AI engineering capability. This role is ideal for someone who understands enterprise architecture, Generative AI, AI agents, coding assistants, DevOps, governance, platform engineering, and software delivery transformation. The AI Architect will design AI-powered engineering frameworks, build reusable agent capabilities, automate SDLC workflows, and help organizations adopt AI safely and effectively across engineering, architecture, QA, DevOps, security, product, and leadership teams. This is a vendor-neutral role, requiring broad hands‑on experience across multiple AI ecosystems, platforms, model providers, coding assistants, agent frameworks, and enterprise delivery tools. What you'll do: 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 clear 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, context, 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. What we're looking for: 8 to 10+ years of experience in software engineering, enterprise architecture, solution architecture, platform engineering, DevOps, or technology transformation. 5+ years designing enterprise-scale architecture, engineering platforms, or developer productivity solutions. 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. Experience designing governance frameworks, reference architectures, standards, reusable playbooks, operating models, and technology roadmaps. Ability to work across different customer environments without forcing a single vendor, platform, or toolset. Strong communication skills with the ability to explain complex AI and architecture concepts to both technical and executive audiences. Platform experience we value: Candidates should bring practical exposure to multiple categories such as: Conversational and Enterprise AI: Microsoft Copilot, ChatGPT Enterprise, Claude, Gemini, Amazon Q, Perplexity Enterprise, or similar platforms. AI Coding Assistants: GitHub Copilot, Claude Code, Cursor, OpenAI Codex, Amazon Q Developer, Gemini Code Assist, Sourcegraph Cody, or comparable tools. Agentic AI and Orchestration: Copilot Studio, Semantic Kernel, LangChain, LangGraph, CrewAI, AutoGen, OpenAI agent frameworks, Claude agent capabilities, or similar technologies. Model and Cloud Ecosystems: Azure AI, AWS Bedrock, Google Vertex AI, OpenAI APIs, Anthropic APIs, Hugging Face, Cohere, or similar ecosystems. Delivery Platforms: Azure DevOps, GitHub, GitLab, Jira, ServiceNow DevOps, Jenkins, Harness, CircleCI, or similar software delivery platforms. Preferred Experience: Designing AI‑assisted software engineering frameworks or AI Centers of Excellence. Building custom skills, agents, plugins, copilots, extensions, commands, or AI-powered workflow automations. Creating agent-based workflows that connect with repositories, work management systems, CI/CD pipelines, documentation platforms, and enterprise knowledge sources. Automating SDLC processes across planning, development, testing, review, deployment, operations, and documentation. Developing context engineering, retrieval, grounding, repository intelligence, and knowledge architecture strategies. Designing multi‑agent systems, closed‑loop remediation workflows, and human approval checkpoints. Leading AI adoption, developer productivity, platform modernization, DevSecOps, or engineering transformation initiatives. Measuring adoption, engineering productivity, software quality, risk reduction, cycle time improvement, and developer experience. Success in this role looks like: Customers have a practical, governed, and vendor‑neutral AI engineering framework. Engineering teams are using AI‑assisted SDLC workflows safely and consistently. Reusable AI agents, skills, prompts, playbooks, and automation frameworks are adopted across teams. Delivery cycle time, software quality, documentation accuracy, test effectiveness, and developer productivity improve measurably. AI governance, security, privacy, compliance, auditability, and human oversight are embedded into the operating model. Teams are trained, enabled, and confident in using AI responsibly across the software delivery lifecycle. Benefits: 401(k). Dental Insurance. Health insurance. Vision insurance. We are an equal‑opportunity employer and value diversity, equality, inclusion, and respect for people. The salary will be determined based on several factors, including, but not limited to, location, relevant education, qualifications, experience, technical skills, and business needs. Additional Responsibilities: Participate in OP monthly team meetings and participate in team‑building efforts. Contribute to OP technical discussions, peer reviews, etc. Contribute content and collaborate via the OP‑Wiki/Knowledge Base. Provide status reports to OP Account Management as requested. Why Join OrangePeople? At OrangePeople, you will help shape the next generation of enterprise AI adoption. This role allows you to work at the intersection of AI strategy, enterprise architecture, software engineering, automation, governance, and transformation. You will help customers move beyond isolated AI tools and build repeatable, secure, scalable, and business-aligned AI engineering capabilities. If you are passionate about Generative AI, agentic workflows, developer productivity, governance, and the future of software engineering, we would love to connect with you. #J-18808-Ljbffr TransSIGHT