Lead Application & Product Architect

Position Summary
We are seeking a highly skilled Data, AI/ML & Master Data Management (MDM) Lead Application & Product Architect (T4) with deep expertise in enterprise data architecture, master data management, AI/ML technologies, and modern data platform design. This role is responsible for defining and governing enterprise data architecture strategy, designing scalable MDM and AI-enabled data platforms, and driving the adoption of intelligent data management capabilities across the organization.

The architect will lead the design of trusted master data solutions, data governance frameworks, knowledge graphs, and AI-powered data stewardship capabilities while enabling enterprise adoption of Machine Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and intelligent automation. The role requires a strong balance of data architecture, AI innovation, and enterprise platform leadership to build scalable, governed, and business-aligned solutions that deliver high-quality, interconnected, and actionable enterprise data.

For energy and industrial domains, the architect will drive the implementation of industry-standard data models, asset-centric master data platforms, and AI-powered data intelligence solutions that support operational excellence, digital transformation, and advanced analytics across the value chain.

Education
Bachelor’s degree in Engineering, Computer Science, or equivalent. Master’s degree preferred.

Must Have
• 14+ years of software engineering experience with 10+ years in AI/ML, data, or intelligent systems.

• Experience architecting AI-enabled MDM platforms leveraging Knowledge Graphs, RAG, Entity Resolution, and OSDU-compliant energy data models for Oil & Gas enterprises.
• Proven experience architecting and delivering enterprise-scale AI products and platforms.
• Expertise in Large Language Models (Azure OpenAI, OpenAI, Claude, Gemini, etc.).
• Strong understanding of AI/ML architecture patterns, enterprise integration, and cloud-native design.

LLM & RAG Architecture
• Deep expertise in prompt engineering, RAG architectures, embeddings, vector databases, context management, and hallucination mitigation.
• Experience designing scalable semantic search and knowledge retrieval platforms.

Agentic AI & MCP Architecture
• Expertise in designing single-agent and multi-agent architectures.
• Experience defining agent orchestration, reasoning, planning, memory, and human-in-the-loop frameworks.
• Experience architecting MCP-compatible tools, services, and interoperability standards.

Agent Capabilities

• Experience implementing:
  • Tool-using agents (function calling, API integrations)
  • Planning and reasoning workflows (ReAct or similar patterns)
  • Agent orchestration and workflow automation
  • Building or integrating MCP-compatible servers/tools
• Understanding of:
  • Memory models (short-term, long-term, vector memory)
  • Human-in-the-loop systems
  • AI guardrails, safety, and observability

Machine Learning: Experience applying AI/ML techniques for entity resolution, intelligent data matching, data quality optimization, metadata enrichment, semantic search, and knowledge discovery across enterprise data platforms.

Architecture & Engineering Leadership
• Define architecture standards, reference architectures, and governance practices.
• Strong expertise in scalability, security, resiliency, observability, and performance engineering.
• Experience with Azure AI ecosystem, cloud-native services, distributed systems, and enterprise integration.
• Experience with Python, AI frameworks, API ecosystems, event driven system, GraphQl and modern software architecture practices.

• Data Platform- Databricks

• MDM Architecture (Golden Record, Entity Resolution)

• Exposure to multimodal AI and knowledge graph technologies. E.g. Neo4j, Neptune,  Cosmos DB Gremlin.

• Ontology design
• Taxonomy management
• Semantic data modeling
• Entity relationship discovery
• Graph-based entity resolution

• Knowledge of MLOps, CI/CD, Responsible AI, and AI governance.

Nice to Have
• Fine-tuning and custom model development experience.•
• Experience with ReactJS, .NET, C#, and enterprise application modernization.

• Knowledge of MDM domains:

• Customer / Account
• Supplier / Vendor
• Asset / Equipment
• Location / Facility
• Product / Material
• Legal Entity
• Organization hierarchy

Key Responsibilities
• Define enterprise AI/ML and GenAI architecture strategy and roadmap.
• Design scalable AI-first platforms, RAG ecosystems, agentic frameworks, and intelligent automation solutions.
• Establish architecture standards, design patterns, governance controls, and best practices.
• Lead architecture reviews and provide technical leadership across multiple teams.
• Evaluate emerging AI technologies and define adoption strategies.
• Partner with business, product, security, and engineering leaders to align AI initiatives with enterprise goals.
• Drive architecture decisions for performance, security, compliance, reliability, and cost optimization.
• Mentor architects and senior engineers on AI architecture and engineering excellence.
• Guide platform engineering teams in implementing scalable and reusable AI capabilities.

• Define enterprise information architecture and MDM roadmap

•  Establish reference architectures and governance standards

•  Evaluate and select strategic technology vendors

•   Influence executive stakeholders and business leadership

What We're Looking For
• Strategic technology leader with strong enterprise architecture experience.
• Ability to translate business objectives into scalable AI architecture solutions.
• Strong balance of architecture vision, technical depth, and execution leadership.
• Passion for innovation and enterprise-scale AI transformation.

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.