Job Description

Group Chief Technology Officer

AI & Decision Intelligence 

Doha, Qatar | Full-Time

The Opportunity

An award-winning, AI-native Decision Intelligence company is building advanced technology for the private-markets investment ecosystem.

Its platform combines data, inference, institutional memory and continuous learning to generate quantitative signals and probabilistic foresight. The technology is designed to improve underwriting and capital allocation over time, with each decision contributing to an evolving intelligence layer that can strengthen subsequent decision-making.

 

 It is now establishing its global headquarters and principal operating hub in Doha, Qatar, creating a major technology and R&D centre in the region.

The company has:

• Completed 70+ engagements across 11 countries

• Developed an AI-native Decision Intelligence platform for sophisticated investment workflows

• Entered its next phase of international growth and R&D expansion

• Begun building its global organization in Doha

• Is expanding institutional market penetration ahead of a planned Series A financing

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IMPORTANT: DOHA BASED

This role requires the successful candidate to be based in Doha, Qatar full-time.

Relocation to Doha is a core requirement of the position.

This is not a remote CTO role and the successful candidate will not be able to operate primarily from another country.

The Group CTO will be expected to relocate to Doha and work from the company's global headquarters, operating closely with the CEO, executive leadership team, R&D organization, investors, institutional customers and strategic technology partners.

Candidates must therefore be comfortable making a permanent or long-term relocation to Doha, Qatar as part of accepting the position.

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The Role

Reporting directly to the CEO and sitting on the executive leadership team, the Group CTO will set technology strategy, architecture and engineering standards across the organization and lead the global R&D function.

The mandate is both strategic and operational.

You will drive innovation, establish execution discipline and ensure that the company's AI and Decision Intelligence capabilities remain at the forefront of the market while translating research into reliable, scalable production systems.

This is a technology leadership role covering:

• AI research and applied AI

• Machine learning

• Agentic systems

• Decision Intelligence

• Architecture

• Engineering

• Product and R&D integration

• Technology strategy

• Organizational design

• Talent leadership

• Investor and customer communication

• Board-level technology communication

Five capabilities will be particularly important:

Planning | Execution | Pitching | Management | Leadership

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Key Responsibilities

Technical Leadership

• Define and own the Group technology strategy, architecture, technical roadmap and engineering standards.

• Translate company strategy into a clear 12-month rolling technology roadmap.

• Convert strategic priorities into specifications, programmes, resources, milestones, dependencies, owners and measurable outcomes.

• Maintain continuous visibility across technology execution.

• Identify risks, dependencies and constraints early and act decisively to keep delivery on track.

• Lead AI, Decision Intelligence, software engineering and R&D initiatives from research and experimentation through to production.

AI & Decision Intelligence

Lead the development of advanced Decision Intelligence capabilities, including:

• Agentic AI

• World models

• Ontology-driven reasoning

• Bayesian and probabilistic inference

• Neuro-symbolic AI

• Knowledge graphs

• Predictive models

• Scoring and ranking

• Uncertainty

• Provenance

• Institutional memory

• Continuous learning

The successful candidate must understand how these technologies work individually and how they can be combined within a production architecture.

World Models & Ontologies

Own the architecture and evolution of the world models and ontologies underpinning the platform.

This includes defining how:

• Entities

• Relationships

• Events

• States

• Time

• Causal structures

• Investment concepts

• Domain knowledge

are represented, connected and continuously updated.

The objective is to enable AI systems to reason over complex private-market environments rather than simply retrieve or generate information.

Predictive Science & Validation

Own the scientific rigour behind predictive capabilities, including:

• Probabilistic forecasting

• Calibration

• Backtesting

• Benchmarking

• Out-of-sample validation

• Signal evaluation

Establish whether Decision Intelligence signals demonstrate genuine and persistent predictive power and ensure that claims of performance are supported by empirical evidence.

Research & Development

• Build a disciplined R&D function capable of identifying, testing and integrating advances in AI and machine learning.

• Maintain a clear distinction between research, prototype, production candidate and production-ready technology.

• Build a high-performance environment for AI researchers, data scientists and software engineers.

• Establish rigorous technical and scientific evaluation standards.

Engineering & Delivery

• Establish rigorous development, testing, release and observability disciplines.

• Ensure that development begins with sufficient definition.

• Establish explicit acceptance criteria and delivery standards.

• Ensure commitments are grounded in engineering evidence.

• Drive decisions, resolve blockers and maintain delivery pace.

Architecture & Infrastructure

Own the end-to-end technical architecture across:

• Models

• Agents

• Data

• Retrieval

• Ontologies

• World models

• Knowledge graphs

• Application services

• Cloud infrastructure

while ensuring scalability, reliability, performance, security and cost efficiency.

Product & R&D Integration

Establish a tightly integrated operating model between Product and R&D.

Translate product strategy, customer requirements and investment workflows into clear technical specifications and differentiated technology.

Maintain explicit accountability for:

• What should be built

• How it should be built

• Who owns it

• When it should be delivered

• How success will be measured

Management & Organization

• Build and run a high-performance operating system for R&D.

• Establish clear objectives, roles, priorities, processes and decision rights.

• Own resource allocation, prioritization and performance management.

• Establish operating cadence and accountability across a distributed R&D organization.

• Build, mentor and manage a world-class team of data scientists, AI researchers and software engineers across multiple R&D centres.

Leadership & Talent

• Recruit exceptional technical talent.

• Develop future technology leaders.

• Set demanding technical and organizational standards.

• Build a culture characterized by intellectual honesty, ambition, urgency, accountability and excellence.

• Create an environment where exceptional people can perform at their highest level.

Executive, Board & Investor Engagement

The CTO will be one of the company's principal external technology voices.

You will be expected to communicate and defend the technology to:

• Institutional investors

• Customers

• Board members

• Strategic partners

• Universities and research institutions

• Technology partners

• Highly technical senior executives

You must be capable of explaining complex technology at both strategic and highly technical levels.

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What We Are Looking For

Essential Requirements

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Probability or a closely related quantitative field from a leading university.

• 10+ years of senior technology and engineering experience.

• Deep and demonstrable leadership across AI, machine learning and production software systems.

• Demonstrated experience architecting and delivering complex AI systems into production.

• Exceptional planning and execution credentials.

• Experience translating technology strategy into multi-quarter roadmaps and delivering against them.

• Demonstrated ability to manage multiple complex technical programmes simultaneously.

• Exceptional presentation and pitching capability.

• Experience communicating complex technology to C-level executives, Boards, investors, customers and technical audiences.

• Demonstrated management experience at organizational scale.

• Experience building and managing multidisciplinary and geographically distributed technical teams.

• Demonstrated leadership capability, including recruiting, developing and retaining exceptional technical talent.

• Deep experience in financial services, asset management, investment banking, private equity, venture capital or adjacent private-markets environments.

• Ability to engage credibly with sophisticated investment professionals and investment workflows.

• Demonstrated expertise in the architecture and application of world models and ontologies.

• Deep expertise spanning LLMs and agentic AI.

• Strong understanding of Bayesian and probabilistic reasoning.

• Strong understanding of neuro-symbolic AI and knowledge-graph methods.

• Experience with RDF, OWL and SPARQL or comparable semantic technologies.

• Strong understanding of predictive modelling, probabilistic forecasting, calibration, backtesting and empirical validation.

• Strong modern software architecture, distributed systems, APIs, data infrastructure, cloud architecture, security and production engineering knowledge.

• Ability to move fluently between research, architecture, engineering, product, program execution and business discussions.

• Exceptional written and verbal English communication skills.

• Willingness and ability to relocate to Doha and work full-time from Doha.

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Technical Expertise

World Models

Deep expertise in computational representations of complex domains that enable AI systems to maintain and reason over entities, relationships, events, states, temporal change, uncertainty and causal structure.

Ontology Engineering

Deep expertise in:

• Ontology design

• Semantic modelling

• Taxonomy and schema design

• Entity and relationship modelling

• RDF

• OWL

• SPARQL

• Knowledge graphs

• Retrieval

• Reasoning

Agentic AI

Fluent in modern agentic AI architecture, including:

• RAG

• Tool use

• Multi-agent systems

• Controller-executor patterns

• Context engineering

• Agent memory

• LangGraph/LangChain-class orchestration

Probabilistic & Neuro-Symbolic AI

Strong command of Bayesian and probabilistic reasoning, uncertainty, calibration and the integration of statistical learning with explicit knowledge and reasoning.

Predictive Science

Deep understanding of:

• Predictive modelling

• Probabilistic forecasting

• Calibration

• Backtesting

• Benchmark construction

• Out-of-sample validation

• Signal evaluation

with the scientific discipline required to distinguish persistent predictive power from noise, leakage or overfitting.

LLM & ML Evaluation

Strong understanding of:

• Retrieval evaluation

• Model comparison

• Hallucination and error analysis

• Uncertainty

• Calibration

• Production monitoring

Knowledge Graphs & Retrieval

Strong command of search, retrieval and graph infrastructure, including Elasticsearch and Neo4j-class systems and their integration with knowledge graphs, ontologies, retrieval and reasoning.

Decision Intelligence

Deep understanding of architectures combining:

• Prediction

• Scoring

• Ranking

• Probabilistic inference

• Rules

• Optimization

• Causal reasoning

to support decisions rather than simply retrieve information or generate content.

MLOps & Production AI

Strong grounding in:

• CI/CD

• Model and prompt versioning

• Evaluation pipelines

• Observability

• Monitoring

• Retraining

• Production deployment

• Azure/Databricks-class infrastructure

Software Engineering

Sufficient command of Python, TypeScript, APIs, application architecture and modern web frameworks to set standards, interrogate technical decisions and review architecture credibly.

This is not necessarily a day-to-day coding role, but it is emphatically a hands-on technical leadership position.

Document Intelligence

Strong understanding of large-scale document ingestion and processing, particularly for unstructured financial and transaction materials, including:

• Extraction

• Chunking

• Retrieval

• Provenance

• Structured knowledge generation

AI Economics

Practical understanding of:

• Token costs

• Inference costs

• Model selection

• Latency

• Compute utilization

• Architectural trade-offs

• Commercial AI economics

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Leadership Characteristics

The organization values leaders who demonstrate:

• Exceptional intellectual honesty

• High EQ

• Low ego

• Strong relationship orientation

• Adaptability and introspection

• Ownership

• Decisiveness

• A bias toward action

• Evidence-based decision making

• The ability to make difficult decisions with incomplete information

• A willingness to change course when evidence changes

• A relentless drive to push the boundaries of AI and Decision Intelligence without sacrificing engineering rigour

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What Success Looks Like

The successful CTO will:

• Establish a clear and executable technology strategy.

• Build and lead a world-class AI and engineering organization.

• Translate advanced AI research into reliable production technology.

• Establish rigorous technical and scientific evaluation standards.

• Build technology capable of supporting sophisticated investment workflows.

• Create a high-performance R&D operating model.

• Communicate complex technology convincingly to investors, customers and the Board.

• Maintain technical credibility at the deepest architectural and AI level.

• Balance technological ambition with engineering discipline and commercial economics.

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Location

Doha, Qatar | Full-Time

Relocation to Doha is mandatory for this position.

The successful candidate must be prepared to relocate to Doha, Qatar and work from Doha full-time.