Enterprise AI Architect

Enterprise AI Architect

Radwell is accelerating its AI journey to drive automation, augmentation, and innovation across commercial, digital, operations, and data domains. As the Enterprise AI Architect, you will serve as a hands-on technical leader and strategic influencer by owning the end-to-end AI architecture and enabling production-grade AI solutions that transform Radwell's business. This is a critical role that combines deep technical expertise with architectural vision and leadership to operationalize AI at scale.

The end-to-end AI architecture will span across data foundations, existing platforms for CRM ERP, contact center, MLOps/ModelOps/LLMOps, governance, security, and solution design to scale AI products and agents that materially impact revenue growth, margin, efficiency, and customer experience. You will work cross-functionally with Digital & eCommerce, Sales/ISR/OSR, Operations, Finance/AR, Procurement, Marketing, and the Data & AI CoE, including tight collaboration with rest of the organization. Radwell has an adopted Enterprise AI Reference Architecture plus reusable reference code and delivery playbooks.

Grounded in Radwell's strategic AI framework, the architect is expected to design platforms and solutions that enable measurable outcomes such as:

• Direct impact on enterprise transformation and board-level priorities.
• Shapes Radwell's AI reference architecture, governance, and delivery model.
• Drives measurable outcomes in AR recovery, ISR automation, procurement, Operations, marketing, pricing, and digital commerce.
• Acts as a bridge between strategy and execution, ensuring AI initiatives deliver tangible ROI.
• AR cash recovery agent: Reduce past-due AR from ~40% to <5% through AI-enabled workflows and decisioning
• Inbound ISR customer inquiries agent: Achieve 80%+ AI augmentation for routine inquiries, with seamless human-in-the-loop transitions
• Automated Procurement & Demand Planning agents: Target 80%+ automation for repetitive tasks, supplier follow‑ups, and inventory actions
• Digital Marketing automation: Enable 90%+ content generation and personalization at scale while preserving brand voice and compliance
• Dynamic pricing models: Optimize elasticity using demand signals and inventory positions; operationalize controlled experimentation and guardrails.
• Punchout/eProcurement analytics & agents: Scale from ~75 to 150+ customers with site/location‑level accountability and automated OSR reporting
• AI-generated product assets: Establish pipelines to generate/upgrade images/videos for tens of millions of SKUs with quality and metadata standards.

Essential duties and responsibilities include:

• Enterprise AI Architecture (Strategic Ownership)
• Define and maintain Radwell's Enterprise AI Reference Architecture (GenAI + Agentic AI + ML + analytics), aligned to an Azure-first strategy and Radwell's application landscape.
• Establish and enforce architecture standards for:
• RAG / enterprise search (knowledge ingestion, chunking strategy, grounding, citations)
• Vector/embedding approach (data ownership, tenancy, lifecycle, retention)
• Agentic workflows (tool use, orchestration, approvals, human-in-the-loop)
• Model governance (routing, versioning, evaluation, deprecation, rollbacks)
• Own architecture decisions for AI across core platforms: ERP (e.g., Epicor P21 / other ERPs), Salesforce, Genesys, eCommerce, and internal apps.
• Hands-On Delivery Leadership (Build & Ship)
• Personally lead design and build reference implementations that teams can reuse (starter kits, SDKs, templates, CI/CD patterns).
• Partner with product + engineering to deliver AI solutions for prioritized Radwell use cases such as:
• Pricing intelligence & optimization
• Parts identification from unstructured content (emails, PDFs, tickets)
• Customer support automation (Genesys + knowledge + next-best-action)
• Demand forecasting & inventory optimization
• Sales enablement / quote assistants integrated with CRM/ERP
• Provide technical leadership through architecture reviews, pairing, and escalation handling; ensure delivery quality from prototype → production.
• AI Platform Engineering: MLOps /AIOps/ LLMOps (Production Readiness)
• Design and implement MLOps/LLMOps pipelines: prompt/model versioning, automated evaluation, model registry, deployment strategies, rollback mechanisms.
• Define AI observability standards: response quality, latency, error rates, grounding accuracy, drift, cost and token consumption, safety events.
• Drive FinOps for AI: model selection, caching, batching, throttling, routing, and cost guardrails tied to budgets and business value.
• Data Foundations, Governance & Responsible AI (Risk + Trust)
• Partner with Data Engineering on AI-ready foundations: data quality, lineage, metadata, MDM, access controls, and domain-aligned data products.
• Establish Responsible AI practices:
• PII handling/redaction, secure prompt & data policies, audit trails
• Security-by-design (least privilege, secrets management, isolation)
• Safety controls and human approvals for high-impact actions
• Coordinate with Security/Compliance on vendor and platform governance.
• Technical Leadership Across Teams (Enablement)
• Mentor engineers and architects across the US and Bengaluru ISSC; raise AI engineering maturity.
• Produce and maintain high-quality technical artifacts: architecture diagrams, reference code, standards, decision records, and runbooks.
• Act as a trusted advisor to executives on AI tradeoffs, build/buy decisions, and delivery sequencing.

Metrics

Complete assigned sprint commitments and project deadlines as agreed and described in regular meetings or through other project planning efforts. Effectively accomplish and adhere to development standards, policies, and quality. Enterprise AI Architect will be measured on the quality, accuracy and timeliness of code releases and on how effectively they accomplish their tasks when compared to the set objectives.

Supervisory Responsibilities

• Does this position have hire/fire authority? No
• Does this position write reviews? No
• Number of Employees Supervised_______
• Number of Subordinate Supervisors Reporting to This Job: _____
• List All Job Titles that Report to This Job: None

Qualifications

• Proven track record as a hands-on technical leader (coding, designing, reviewing, and shipping).
• Ability to influence across engineering and business stakeholders with clear technical communication.
• Strong executive communication, stakeholder management, and governance leadership.
• Demonstrated ability to drive outcomes in complex enterprise environments (ERP/CRM/contact center integrations).
• Builder mindset: can create structure from ambiguity and scale with speed.
• Customer/value obsessed: ties engineering work directly to measurable business results.
• Faster, safer AI delivery across teams due to consistent patterns and strong technical leadership.

Knowledge & Skills Required

• Strong experience in enterprise integration across major platforms (ERP/CRM/contact center/eCommerce) and modern patterns (API-first, event-driven).
• Practical expertise in:
• GenAI patterns: RAG, embeddings, prompt engineering, agent/tool use, evaluation
• Production engineering: CI/CD, DevSecOps, observability, SRE mindset
• Data architecture: lake/lakehouse concepts, governance, metadata, MDM
• Azure AI ecosystem: Azure OpenAI, Azure AI Studio/Azure ML, Databricks/Synapse/Fabric, API Management, Key Vault, Entra ID.
• Experience with vector databases and enterprise search platforms; semantic layers / knowledge graphs.
• Experience with agent orchestration frameworks (e.g., Semantic Kernel, LangChain/LangGraph or equivalent).
• Strong background in modern product engineering: Agile delivery, microservices, APIs, event-driven patterns.
• Hands-on understanding of Cloud, DevOps, CI/CD, Observability/AIOps, and security fundamentals.
• 10+ years experience with REST, GraphQL, API design patterns, scalable containerized systems, and microservices.
• 10+ years or mastery in