AI Architect

Orange People 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, Dev Ops, 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, Dev Ops, 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.

• 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.

• 8 to 10+ years of experience in software engineering, enterprise architecture, solution architecture, platform engineering, Dev Ops, 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, Dev Ops, Dev Sec Ops , 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.

• 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:
    Git Hub 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, Lang Chain, Lang Graph, CrewAI, Auto Gen, 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 Dev Ops, Git Hub, Git Lab, Jira, Service Now Dev Ops, Jenkins, Harness, CircleCI, or similar software delivery platforms.

• 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…