Ai Architect

We are looking for an experienced
AI Architect
to design and build an enterprise-grade, AI-enhanced software engineering platform that improves developer productivity, code quality, and engineering effectiveness.
This is a
hands‑on architecture and platform engineering role
focused on integrating enterprise AI capabilities into software development workflows. The ideal candidate combines strong enterprise architecture skills with practical experience building secure, scalable, governed AI platforms using managed LLM services.
Enterprise AI Architecture & Platform Engineering
– Designing and productionizing secure, reusable AI platforms using managed foundation‑model services.
LLM Integration & Governance
– Governed model access, AI gateways, model routing, authentication, usage controls, and cost monitoring.
GitHub Copilot Enterprise
– Enterprise adoption, model selection, licensing, usage governance, and developer workflow integration.
Context Engineering
– Prompt design, token management, structured outputs, tool calling, grounding, citations, and workflow orchestration.
RAG & Vector Search
– Retrieval‑augmented generation, embeddings, chunking, hybrid search, retrieval evaluation, Azure AI Search, and PostgreSQL with pgvector.
Developer Toolchain Integration
– Azure DevOps, GitLab, GitHub, Jira, Confluence, REST APIs, webhooks, CI/CD, and software development workflows.
Cloud Security & AI Governance
– Microsoft Entra ID, managed identities, Azure Key Vault, least‑privilege access, prompt‑injection defence, privacy, and auditability.
Observability & Production Readiness
– OpenTelemetry, Azure Application Insights, telemetry, performance monitoring, reliability, and cost optimization.
Enterprise Knowledge Architecture
– Knowledge modelling, metadata, taxonomies, provenance, and knowledge graphs where appropriate.
Define the
target‑state AI architecture, implementation roadmap, and prioritized MVPs based on business value, risk, and delivery effort.
Design and implement reusable platform capabilities for governed LLM access, context engineering, enterprise knowledge retrieval, workflow orchestration, evaluation, and observability.
Integrate AI capabilities into developer workflows, initially leveraging GitHub Copilot and Azure DevOps repositories, work items, pipelines, and service hooks.
Design
tool‑agnostic integration patterns
to support evolving engineering ecosystems, including GitLab, Jira, and Confluence.
Establish enterprise architecture standards, security controls, reference implementations, design documentation, and operational runbooks.
Collaborate with Information Security, Cloud Engineering, Privacy, Risk, Architecture Review Board, and AI governance stakeholders.
Partner with engineering teams through hands‑on implementation, technical guidance, design reviews, and knowledge transfer.
Define evaluation and monitoring practices to measure AI quality, developer productivity, reliability, security, and cost effectiveness.
Required Experience
Proven experience in
enterprise AI architecture, AI platform engineering, and production LLM integration .
Hands‑on experience with managed LLM services and enterprise AI governance patterns.
Strong understanding of
RAG, vector retrieval, context engineering, and AI workflow orchestration .
Experience integrating enterprise platforms through APIs, webhooks, and modular abstraction layers.
Strong knowledge of
Azure cloud security, identity management, data protection, and observability .
Experience with modern software engineering toolchains, CI/CD, source control, and application security practices.
Ability to translate enterprise requirements into secure, scalable architectures and deliver pragmatic, production‑ready MVPs.
Excellent communication and stakeholder management skills, with the ability to influence architecture decisions and mentor engineering teams.
What Makes This Role Unique?This role offers the opportunity to shape an enterprise AI platform from architecture through implementation, enabling responsible AI adoption across software engineering teams.
The focus is on
building practical AI capabilities, not training AI models - connecting approved LLMs, enterprise knowledge, and engineering tools through secure, reusable, and measurable platform services.
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