PwCPosted 5mo ago

ETIC, AI Architect - Director level

ETIC, AI Architect - Director level at PwC scores 75 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

CairoexecutiveFull time

AI in this role

openailangchainllamaindexlanggraphcrewaiautogensemantic-kernelbedrockvertex-aiazure-openaimlflowsagemaker+1

ragml-opsai-automationai-safety

Line of Service

Advisory

Industry/Sector

Technology

Specialism

Advisory - Other

Management Level

Director

Job Description & Summary

The Director (D) for AI & Intelligent Automation will define and execute the enterprise strategy for Artificial Intelligence, Machine Learning, and Automation across business domains.
This role blends technical excellence, strategic leadership, and commercial acumen, combining deep expertise in Python, .NET, and cloud-native architectures to deliver scalable, secure, and value-generating intelligent systems – leveraging the latest in thinking in the future agentic web.
The MD/D will partner with C-suite executives, technology leaders, and global delivery teams to embed AI capabilities at scale—accelerating innovation, enhancing decision-making, and transforming enterprise operations.

Key Leadership Responsibilities

Strategic Vision & Governance

Define the global AI & Intelligent Automation strategy, ensuring alignment with enterprise digital transformation and innovation objectives.

Establish technical governance frameworks for AI ethics, model transparency, and Responsible AI implementationServe as the senior executive sponsor for AI architecture, operating model, and adoption roadmap.

Experience delivering solutions leveraging
Experience delivering intelligent, agentic solutions leveraging frameworks such as Semantic Kernel, LangGraph, CrewAI, LangChain, AutoGen and LlamaIndex, with a strong focus on orchestration, multi-agent collaboration, and scalable AI-driven architectures.

Drive responsible integration of Large Language Models (LLMs) from multiple providers including deployment via Azure OpenAI Service, Amazon Bedrock or Vertex AI.

Implement retrieval-augmented generation (RAG) architectures and manage vector databases

Data Platform & Engineering Excellence

Lead the evolution of the enterprise data estate, leveraging modern data platforms such as Databricks, Snowflake, Azure Synapse, AWS Glue/Redshift and BigQuery.

Oversee data engineering using Apache Airflow, dbt, and Prefect, ensuring data pipelines are performant, governed, and aligned with enterprise metadata standards (Collibra, Alation, Microsoft Purview).
Ensure high-quality, compliant data foundations for machine learning and analytics workloads.

Cloud, Infrastructure & MLOps

Champion multi-cloud architecture and engineering excellence across Azure, AWS, and GCP.

Lead enterprise MLOps initiatives using Azure ML, SageMaker, Vertex AI, MLflow, and Kubeflow

Intelligent Automation & Cognitive Services

Drive end-to-end intelligent automation using Power Automate, Blue Prism, and Automation Anywhere.

Integrate cognitive services including Azure Cognitive Services, AWS Comprehend, Form Recognizer, and Speech/Translation APIs to augment digital workflows.

Lead enterprise process mining and optimization initiatives via Celonis, Power BI Process Mining, and ProcessGold.

Security, Compliance & Responsible AI

Ensure alignment with enterprise security standards and frameworks (SOC2, ISO27001, NIST).

Oversee identity and access management through Azure AD, OAuth2, OpenID Connect, and integration with enterprise IAM systems.

Champion ethical AI, bias detection, and explainability through Azure Responsible AI Dashboard and equivalent frameworks.

Leadership, Talent & Innovation

Build and lead high-performing global teams in data science, engineering, and automation disciplines.

Cultivate a culture of innovation, continuous learning, and responsible experimentation.

Engage with the external AI ecosystem—academic institutions, hyperscalers, and startups—to identify strategic partnerships and emerging opportunities.

Preferred Background

Scaled demonationable experince of delivery or consulting leadership with significant experience delivering enterprise AI, data, and automation transformations.

Proven record integrating Python-based AI with .NET enterprise systems.

Deep expertise across multi-cloud environments, data governance, and enterprise DevSecOps.

Demonstrated ability to deliver large-scale transformation programs and measurable ROI.

Strong executive presence, communication, and client/stakeholder management skills.

Minimum years experience required

Additional application instructions

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required:

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Amazon Web Services (AWS), Analytical Thinking, Architectural Engineering, Brainstorm Facilitation, Business Impact Analysis (BIA), Business Process Modeling, Business Requirements Analysis, Business Systems, Business Value Analysis, Cloud Strategy, Coaching and Feedback, Communication, Competitive Advantage, Competitive Analysis, Conducting Research, Creativity, Embracing Change, Emotional Regulation, Empathy, Enterprise Architecture, Enterprise Integration, Evidence-Based Practice (EBP) {+ 53 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagMl OpsAI AutomationAI SafetyOpenAILangChainLlamaIndexLangGraph

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a workflow you automated with AI tools, end to end.
How do you think about the risk of an AI system in this kind of role failing silently?
Walk me through how you've used OpenAI in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Rag, Ml Ops, AI Automation, AI Safety, and OpenAI. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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