AI Solutions Architect - Agentic Finance & Enterprise AI

Function: Artificial Intelligence | Enterprise Technology | Finance Transformation

About the Role

We are seeking an experienced and technically accomplished AI Solutions Architect to lead the architecture, development, and deployment of next-generation enterprise AI and agentic systems.

This is a senior, hands-on role for an architect who can move beyond AI prototypes and design secure, scalable, production-grade AI platforms capable of executing complex business workflows.

A major focus will be Agentic Finance — designing autonomous and semi-autonomous AI agents capable of supporting finance operations, financial analysis, reporting, controls, reconciliations, forecasting, treasury, compliance, and management decision-making.

The successful candidate will combine expertise in Generative AI, LLMs, multi-agent architectures, enterprise systems integration, cloud architecture, data engineering, and financial workflows.

1. Enterprise AI Architecture

• Define the technical architecture and roadmap for enterprise-wide AI adoption.
• Design scalable architectures integrating LLMs, AI agents, enterprise applications, databases, APIs, and business workflows.
• Establish architecture standards for AI applications covering scalability, security, reliability, observability, governance, and cost.
• Evaluate and select appropriate AI models, frameworks, vector databases, orchestration platforms, and cloud infrastructure.
• Convert business requirements into robust technical architecture and implementation plans.

2. Agentic AI & Multi-Agent Systems

• Architect sophisticated single-agent and multi-agent AI systems capable of planning, reasoning, executing tasks, using tools, and coordinating workflows.
• Design agent orchestration, memory, tool-use, state management, permissions, and human-in-the-loop mechanisms.
• Build agents capable of interacting securely with enterprise systems, APIs, databases, documents, and external information sources.
• Develop mechanisms for agent supervision, validation, escalation, audit trails, and failure recovery.
• Establish evaluation frameworks to measure agent accuracy, reliability, latency, cost, and task-completion performance.

3. Agentic Finance Solutions

Lead the architecture of AI-powered finance workflows including:

• Accounts Payable and Accounts Receivable automation
• Invoice processing, matching, and exception handling
• Bank and ledger reconciliations
• Month-end and financial closing workflows
• Management reporting and variance analysis
• Financial statement analysis
• Cash-flow forecasting
• Working-capital optimization
• Treasury and liquidity monitoring
• Budgeting and forecasting
• Financial controls and anomaly detection
• Audit support and document review
• Procurement and payment workflow intelligence
• Credit and counterparty analysis
• Automated financial data extraction and validation

Design AI agents that can analyze financial information, identify exceptions, prepare recommendations, and initiate approved workflows while maintaining appropriate human authorization and financial controls.

4. Generative AI & LLM Engineering

• Architect enterprise applications using leading commercial and open-source LLMs.
• Design RAG (Retrieval-Augmented Generation) architectures using enterprise documents and structured data.
• Implement semantic search, embeddings, vector databases, reranking, and knowledge retrieval systems.
• Develop structured prompting, tool/function calling, context management, and model-routing strategies.
• Design model evaluation and benchmarking frameworks.
• Optimize AI systems for accuracy, latency, token consumption, infrastructure utilization, and operating cost.
• Implement safeguards against hallucinations, prompt injection, data leakage, and unreliable autonomous actions.

5. Enterprise & ERP Integration

Architect secure AI integrations with platforms such as:

• SAP
• Oracle
• Microsoft Dynamics
• ERP and accounting systems
• Banking and treasury platforms
• CRM systems
• Data warehouses and data lakes
• Business intelligence platforms
• Document management systems
• Internal enterprise APIs

Develop architectures enabling AI agents to read, analyze, recommend, and—where authorized—execute actions across enterprise systems.

6. Data & Knowledge Architecture

• Design data pipelines supporting AI and agentic applications.
• Integrate structured and unstructured enterprise information.
• Establish enterprise knowledge layers connecting financial, operational, commercial, and transactional data.
• Define data quality, lineage, access control, retention, and governance requirements.
• Work closely with data engineering teams to create reliable AI-ready datasets.

7. AI Governance, Security & Controls

• Establish enterprise AI governance and responsible-AI architecture.
• Design role-based access controls and agent permission frameworks.
• Implement comprehensive logging and auditability of AI decisions and actions.
• Ensure sensitive financial and corporate information remains protected.
• Define approval thresholds for autonomous actions and human-in-the-loop workflows.
• Design controls preventing unauthorized transactions or changes to financial records.
• Support compliance with applicable data protection, cybersecurity, and corporate governance requirements.

8. Production Deployment & LLMOps

• Lead AI solutions from proof-of-concept through production deployment and enterprise-scale adoption.
• Establish CI/CD and LLMOps/MLOps practices for AI applications.
• Implement monitoring for model performance, hallucination rates, retrieval quality, agent failures, latency, and infrastructure costs.
• Design highly available and resilient AI services.
• Establish testing environments and controlled release processes for autonomous agents.

9. Technical Leadership

• Serve as the organization's technical authority for AI architecture.
• Lead architecture reviews and major technical design decisions.
• Mentor AI engineers, software engineers, data engineers, and technical teams.
• Collaborate with Finance, Operations, IT, Commercial, Risk, Audit, and senior management.
• Evaluate emerging AI technologies and determine their practical enterprise applicability.
• Develop a long-term roadmap for enterprise AI and intelligent automation.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Computer Engineering, or a related discipline.
• 7 years of experience in software engineering, solution architecture, cloud architecture, data engineering, or AI systems.
• 3 years of practical experience working with Generative AI, machine learning, NLP, or LLM-based applications.
• Demonstrated experience architecting and deploying production-grade enterprise AI solutions.
• Strong understanding of distributed systems, APIs, microservices, cloud infrastructure, databases, and enterprise integration.

Technical Expertise

Strong hands-on knowledge of several of the following:

AI & LLMs:

• OpenAI / Azure OpenAI, Anthropic, Gemini, Llama and other commercial or open-source models.

Agentic Frameworks:

• LangGraph, LangChain, AutoGen, Semantic Kernel, CrewAI or equivalent orchestration frameworks.

RAG & Knowledge Systems:

• Vector databases, embeddings, semantic retrieval, hybrid search, reranking, knowledge graphs, and enterprise search.

Programming:

• Strong proficiency in Python; experience with JavaScript/TypeScript or other enterprise development languages is advantageous.

Cloud:

• AWS, Microsoft Azure and/or Google Cloud Platform.

Data:

• SQL, relational databases, NoSQL databases, data warehouses, APIs, ETL/ELT pipelines, and modern data platforms.

Infrastructure:

• Docker, Kubernetes, CI/CD, APIs, microservices, observability, authentication, authorization, and cloud security.

Finance Domain Experience

• Candidates should have strong exposure to enterprise financial processes or demonstrate the ability to architect solutions around:
• General Ledger
• AP / AR
• Financial reporting
• Management accounting
• Budgeting and forecasting
• Treasury
• Banking
• Procurement
• Reconciliation
• Audit and internal controls
• Financial planning and analysis (FP&A)

Previous experience developing AI solutions for finance, banking, fintech, ERP, audit, accounting, treasury, or corporate finance will be highly valued.

Preferred Qualifications

• Master's degree in AI, Computer Science, Data Science, or related discipline.
• AWS, Azure, GCP or AI-related professional certifications.
• Experience integrating AI solutions with SAP, Oracle, Microsoft Dynamics or similar ERP platforms.
• Experience building autonomous or semi-autonomous enterprise agents.
• Experience with financial services, manufacturing, commodities, petrochemicals, trading, or large-scale industrial organizations.
• Experience managing sensitive financial data and mission-critical enterprise systems.

What We Are Looking For

We are looking for a builder and architect not simply an AI researcher or prompt engineer.

The ideal candidate should be capable of taking a complex business process, understanding the underlying systems and controls, and designing an AI architecture that can safely automate substantial portions of that process.

You should be comfortable discussing architecture with engineers, financial workflows with CFO-level stakeholders, security with IT teams, and business outcomes with senior management.

Key Success Measures

Success in this role will be measured by:

• Number of enterprise AI workflows successfully deployed into production
• Reduction in manual finance-processing effort
• Improvement in financial reporting and reconciliation turnaround times
• AI agent accuracy and successful task-completion rates
• Reliability and uptime of AI platforms
• Reduction in AI inference and infrastructure costs
• Successful integration with enterprise and financial systems
• Compliance with security, governance, and audit requirements
• Measurable financial and operational ROI from AI deployments

This is an opportunity to architect the AI foundation of an organization and build intelligent systems that move beyond conversational AI into autonomous enterprise execution.