AI Architect

Role & responsibilities

1. AI & Data Architecture

• Design end-to-end enterprise architectures covering AI, GenAI, ML, Data Engineering and Data Science.
  • Architect LLM, RAG, Agentic AI, AI Copilots and intelligent automation solutions.
  • Define architectures connecting enterprise data, AI models, applications, APIs and business workflows.
  • Design scalable data platforms and AI platforms supporting production workloads.
  • Define technology choices across cloud, data, AI and application layers.
  • Establish architecture standards, reference architectures and technology patterns.
  

2. Generative AI & Emerging Technologies

• Develop enterprise GenAI strategies and solution architectures.
  • Architect solutions using LLMs, RAG, vector databases, knowledge graphs and AI agents.
  • Evaluate commercial and open-source foundation models.
  • Define strategies for model selection, fine-tuning, prompt engineering, embeddings and model evaluation.
  • Identify emerging AI technologies and assess their relevance to enterprise clients.
  • Develop reusable GenAI accelerators and frameworks for the MBS AI Studio.
  

3. Data Engineering

• Define modern data engineering architectures supporting AI and analytics.
  • Architect data lakes, lakehouses, data warehouses and real-time data platforms.
  • Design data ingestion, transformation, integration and processing pipelines.
  • Define architectures for structured, semi-structured and unstructured data.
  • Work with Data Engineers to establish scalable and reliable data pipelines.
  • Integrate enterprise data platforms with AI/ML and GenAI workloads.

4. Practice Development

A significant part of this role will be building and scaling the AI, Data Engineering & Data Science practice at MBS Global.

Responsibilities will include:

• Define the practice vision, capability roadmap and technology strategy.
  • Build a portfolio of AI, Data Engineering and Data Science offerings.
  • Develop industry-specific AI solutions and accelerators.
  • Create reusable reference architectures, frameworks, methodologies and IP.
  • Establish AI/GenAI solution blueprints that can be replicated across clients.
  • Identify strategic technology partnerships with hyperscales, AI platforms and data technology providers.
  • Define competency frameworks and technical career paths.
  • Help recruit, mentor and develop AI Architects, ML Engineers, Data Engineers and Data Scientists.
  • Build communities of practice and technical centers of excellence.
  • Establish delivery standards, architecture governance and quality frameworks.
  • Develop case studies and thought leadership for the AI Studio.
  

5. Presales

The candidate will work closely with MBS Globals sales and leadership teams to develop new AI and data-led business.