Chief AI Architect

Job Description

Insight Global is seeking a highly technical Principal AI Architect who can operate as both a Chief Architect and hands-on AI engineering leader. This individual will define enterprise AI strategy, establish architecture standards, build governed AI platforms and enterprise knowledge systems, drive engineering execution, and ensure AI investments deliver measurable business outcomes.

Day-to-Day Responsibilities

• Define and evolve the enterprise AI strategy, future-state architecture, operating model, and multi-year roadmap.

• Partner with executive leadership to align AI investments to approved business outcomes, success metrics, and adoption goals.

• Architect enterprise AI capabilities across:

o Private AI platforms

o Commercial AI services

o Agent frameworks

o Copilots

o Workflow automation

o Enterprise knowledge systems

o Governance, security, observability, and cost management

• Design and govern a centralized enterprise knowledge layer leveraging semantic models, metadata, taxonomies, ontologies, knowledge graphs, vector databases, and retrieval services.

• Define architecture patterns for RAG, GraphRAG, hybrid retrieval, federated search, reranking, citation generation, and context orchestration.

• Review architecture, code, APIs, retrieval pipelines, model integrations, evaluation frameworks, and production telemetry with engineering teams.

• Build prototypes and establish reference implementations to accelerate delivery and reduce technical ambiguity.

• Lead architecture reviews, technical governance processes, design standards, and enterprise AI platform adoption.

• Establish AI quality, performance, observability, reliability, and cost-management metrics across models, agents, workflows, and knowledge services.

• Evaluate commercial AI platforms and emerging technologies while developing build-versus-buy recommendations and platform rationalization strategies.

• Define model hosting, routing, inference, access control, security, and deployment patterns across public cloud and private AI environments.

• Drive evaluation frameworks covering retrieval quality, groundedness, answer relevance, agent performance, adoption, business outcomes, and platform economics.

• Create executive presentations, architecture decision records, technology roadmaps, business cases, and strategy documentation.

• Mentor engineers and technical leads while establishing repeatable standards, best practices, governance frameworks, playbooks, and operating procedures.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• 10+ years of experience in Enterprise Architecture, AI Engineering, Cloud Architecture, Distributed Systems, Knowledge Systems, or AI Platform Engineering.

• Proven experience designing, architecting, and delivering production-scale enterprise AI platforms, not just AI proofs of concept.

• Strong hands-on technical expertise in:

o Python, APIs, distributed systems, cloud-native architectures, and production engineering practices

o Microsoft Azure AI ecosystem including Azure OpenAI, Azure AI Search, Fabric, Synapse, Azure Machine Learning, AKS, Copilot Studio, Microsoft Graph, Purview, and Entra ID

o RAG, GraphRAG, hybrid retrieval, semantic search, knowledge graphs, vector databases, reranking, and context engineering

o Model serving, inference optimization, quantization, model routing, capacity planning, and private AI deployments

o AI evaluation, observability, monitoring, telemetry, and performance measurement

o AI governance, privacy, security, responsible AI, access controls, and enterprise data governance

• Experience building enterprise AI architectures spanning private AI, commercial AI platforms, copilots, agent frameworks, workflow orchestration, and reusable enterprise knowledge services.

• Deep understanding of enterprise semantic architecture including ontologies, taxonomies, metadata management, semantic layers, data lineage, entity resolution, business glossaries, and knowledge management.

• Experience designing governed enterprise knowledge platforms leveraging structured data, unstructured content, APIs, knowledge graphs, vector indexes, and retrieval services.

• Hands-on experience reviewing architecture, code, retrieval pipelines, model integrations, and production AI systems with engineering teams.

• Experience establishing architecture standards, technical governance, delivery frameworks, and operating models for enterprise AI programs.

• Executive-level communication skills with experience presenting AI strategy, architecture decisions, business cases, and technology roadmaps to senior leadership. • Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Engineering, Information Science, or related field.

• Experience building enterprise-wide AI platforms that support multiple business functions and user populations.

• Experience integrating private AI, commercial AI tools, and enterprise knowledge services through a centralized semantic architecture.

• Familiarity with:

o Agentic AI systems and multi-agent architectures

o Multimodal AI (vision, speech, document intelligence)

o Human-in-the-loop workflows

o Real-time and event-driven AI architectures

o Fine-tuning, LoRA, QLoRA, distillation, synthetic data generation, and model adaptation techniques

• Experience with AI observability and evaluation frameworks such as MLflow, Langfuse, Phoenix, OpenTelemetry, or similar platforms.

• Experience with open-source AI technologies including LangGraph, LlamaIndex, Haystack, vLLM, Ray, LiteLLM, KServe, llama.cpp, Neo4j, pgvector, or comparable tools.

• Experience designing GPU-enabled, hybrid-cloud, sovereign, air-gapped, or private AI environments.

• Experience evaluating and rationalizing AI platforms, copilots, developer assistants, automation tools, and commercial AI products.

• Background in regulated, security-sensitive, data-intensive, or asset-intensive industries.

• Previous experience as a Principal Architect, Chief Architect, AI Transformation Leader, or Enterprise AI Strategy leader.