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AI Architect

Generative AI, Machine Learning, Snowflake and G&A; Domains

Role Summary

We are seeking an experienced AI Architect to define and deliver scalable, secure and responsible AI solutions for our General and Administrative (G&A;) functions, turning AI capabilities into measurable business value.

The ideal candidate will combine robust architecture and technical expertise in generative AI, machine learning, LLM-based and agentic solutions and cloud data platforms (including Snowflake) with practical functional knowledge in one or more G&A; domainsparticularly Finance, Procurement, HR, Corporate, Legal, Compliance, Communications or Business Development.

This is not a purely technical architecture role. The architect must understand business processes, engage effectively with functional stakeholders and translate business problems into governed, production-grade AI solutions that people trust and adopt. Proven solutioning experience and a track record of measurable business impact are mandatory for this role.

Key Responsibilities

- Partner with G&A; stakeholders to understand business processes, challenges and KPIs, and identify high-value AI use cases.
- Define end-to-end AI solution architecturesfrom data and model layers to application, integration and monitoringaligned with enterprise standards.
- Assess and prioritise AI, machine-learning and generative-AI use cases on business value, feasibility, risk and cost, ensuring solutions are secure, explainable and commercially valuable.
- Design LLM-based solutions such as retrieval-augmented generation (RAG), copilots, document intelligence, text-to-SQL and agentic workflows over enterprise data.
- Select and integrate foundation models, vector stores, orchestration frameworks and AI services across cloud and Snowflake platforms (e.g., Cortex AI), balancing build-versus-buy and cost.




- Work with data architects and engineers to ensure AI solutions are fed by governed, high-quality data and semantic models with consistent business definitions.
- Design integrations across ERP, procurement, HRIS, CRM, legal, compliance and other enterprise platforms.
- Establish MLOps and LLMOps practices covering deployment, versioning, evaluation, monitoring, drift detection, cost control and lifecycle management.
- Define responsible-AI standards covering security, privacy, bias, explainability, human-in-the-loop controls, guardrails and regulatory compliance.
- Evaluate solution options and clearly communicate architectural decisions, dependencies and trade-offs.
- Guide engineering and data science teams through implementation and review solutions for alignment with the target architecture.
- Work across business, product, architecture and engineering teams to create practical AI roadmaps and drive adoption.

Required Experience

- Significant experience as an AI, Solution or Enterprise Architect delivering production AI/ML or generative-AI solutions.
- Mandatory: proven solutioning experienceshaping business problems into end-to-end AI architectures, evaluating options, and leading solutions through design, estimation, delivery handover and adoption.
- Mandatory: ability to articulate, with evidence, the business impact created by past AI solutions (e.g., cost savings, productivity or cycle-time gains, improved decision quality, risk reduction, revenue impact) and your specific role in achieving it.
- Strong hands-on knowledge of generative-AI and LLM architecture, including RAG, prompt engineering, evaluation, guardrails,



vector search and agent frameworks.
- Strong experience in machine-learning solution design and MLOps/LLMOps, covering model deployment, monitoring and lifecycle management.
- Demonstrable functional knowledge in at least two G&A; areas, preferably Finance, Procurement or HR.
- Understanding of business processes such as record-to-report, procure-to-pay, budgeting and forecasting, workforce analytics, vendor management or compliance reporting.
- Working knowledge of Snowflake and modern cloud data platforms, including data preparation, semantic models and secure data access for AI workloads.
- Strong knowledge of Python and working knowledge of SQL, APIs and AI frameworks such as LangChain, LlamaIndex or similar.
- Experience integrating AI solutions with enterprise platforms such as ERP, EPM, procurement, HRIS or CRM systems.
- Knowledge of AI security, privacy, responsible-AI, model risk and governance practices, including role-based access control and sensitive-data handling.
- Excellent stakeholder management and communication skills, with the ability to explain AI capabilities, limitations and risks to non-technical audiences.

Preferred Experience

- Architecture experience with AI services on AWS (Bedrock, SageMaker) or Azure (Azure OpenAI, Azure AI Foundry, Azure ML).
- Experience with Snowflake Cortex AI, Snowpark ML or comparable in-platform AI capabilities.
- Experience with agentic AI, tool and protocol integration (e.g., MCP) and multi-agent orchestration.
- Experience with intelligent document processing, conversational AI and AI embedded in BI or self-service analytics.
- Knowledge of AI regulation and standards such as the EU AI Act, NIST AI RMF and ISO/IEC 42001.
- Experience delivering AI solutions in pharmaceutical, life-sciences or another regulated industry.
- Familiarity with enterprise architecture methods and architecture decision records.

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