Lead Architect - AI

About Landmark Digital

Role Overview

Key Focus Areas

• Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria.
• Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers.
• Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows.
• Work with enterprise architects to align solutions with target architectures, integration patterns, platform standards and governance.
• Evaluate models, platforms, frameworks and vendors through structured experiments.
• Guide engineering teams through implementation, architecture and code reviews, integration and production readiness.
• Embed responsible AI and security by design: privacy, access controls, permission-aware retrieval, tenant isolation, prompt-injection defenses, safe tool use, audit trails and human approval for high-impact actions.
• Define evaluation, testing, monitoring and lifecycle controls for models, prompts, retrieval and agents.
• Track the AI landscape and translate developments into practical roadmaps and guidance.
• Communicate decisions clearly to technical teams, product owners and senior stakeholders.

Knowledge, Skills & Experience

• A strong track record in software, solution or AI architecture, with evidence of shipping and operating enterprise-grade products. Indicative experience: 8+ years in engineering/architecture, including 3+ years working on AI/ML solutions.
• Direct experience taking an LLM-based or agentic product into production, beyond demos and proofs of concept.
• Broad understanding of the AI landscape: foundation and open-weight models, conventional ML, RAG, embeddings, retrieval/reranking, context engineering, fine-tuning, multimodal systems and agent orchestration.
• Hands-on ability to prototype and review production code, preferably in Python and at least one product/backend stack.
• Strong API, distributed-system, data-pipeline and enterprise integration design skills.
• Experience with at least one major cloud and its AI services, plus containerized or managed deployment, CI/CD, infrastructure automation, MLOps/LLMOps and observability.
• Practical experience with AI evaluation, grounding quality, safety testing, model/prompt versioning and cost/latency optimization.
• Familiarity with identity, least privilege, data boundaries and secure tool/API access.
• Ability to influence without relying on reporting authority, resolve architectural trade-offs and work effectively with enterprise architects, engineering teams and product owners.
• Clear written and verbal communication: architecture diagrams, decision records, delivery guidance and explanations suitable for business audiences.
• Degree in computer science, engineering or a related field, or equivalent practical experience.

Additional Experience

• Regulated or enterprise environments; AI platform/CoE design; Model Context Protocol (MCP) and other tool-integration patterns; knowledge graphs; model-serving and open-weight deployment; domain-specific AI validation; architecture or cloud certifications; mentoring across multiple product teams.
• Specific frameworks and certifications are advantages, not substitutes for delivery evidence.

What Success Looks Like