Artificial Intelligence Engineer - azakaw

j. awan & partners; Porto; Presencial; EUR 50 000 - 90 000; Tempo integral

Resumo da oferta

azakaw is seeking an Artificial Intelligence Engineer to join the Porto-based engineering team. You will build and ship AI-based capabilities for client onboarding, document review, risk analysis and compliance support within the project AI RegTech and Intelligent CRM for Compliance and Customer Experience.

This is a delivery role, turning business problems into working software quickly with quality and traceability.

Qualificações

• Bachelor’s degree required in a technical field.
• Minimum 3 years building production software with AI features.
• Experience with RegTech, fintech, or compliance is preferred.

Responsabilidades

• Design, build and ship AI-enabled features for onboarding, document review, risk analysis and compliance support.
• Develop retrieval, reasoning and orchestration pipelines, including agentic workflows and tool-calling patterns.
• Collaborate across stack and product areas to deliver production-ready solutions.

Conhecimentos

AI tools

Autonomy

Ownership

Communication

English proficiency

Formação académica

Bachelor’s degree in Computer Engineering/CS/Data Science/Applied Mathematics

Ferramentas

OCR

Descrição da oferta de emprego

Artificial Intelligence Engineer - azakaw

Description

Reports To: Group CTO & Deputy CEO

Direct Reports: None

Location: Porto, Portugal

Type: Full-time

About azakaw azakaw is a purpose-built RegTech compliance platform backed by the regulatory expertise of j. awan & partners, a leading international GRC consultancy headquartered in the Dubai International Financial Centre (DIFC). The platform automates KYC, KYB, AML and ongoing monitoring for regulated institutions across the GCC, Africa, and emerging markets, covering over 1,700 screening lists, more than 24,000 identity document types, liveness and deepfake detection, and national integrations such as Nafath and Yakeen in Saudi Arabia. The engineering function is based in Porto, Portugal.

Culture and Values

• Growth mindset, resilience, accountability, and adaptability in a fast-paced scaling environment.
• Ownership from problem to production. Engineers here decide how the product is built, and carry the consequences of those decisions.
• Professional maturity and integrity in handling sensitive client and regulatory data.
• Pragmatism over novelty. We use AI where it creates real value and conventional engineering where it does not.
• Trust and credibility built through consistent follow-through, transparency, and candid communication.

Mission

A hands-on engineering role responsible for the development, integration and optimisation of Artificial Intelligence solutions applied to azakaw’s client onboarding and client management processes, within the project "AI RegTech and Intelligent CRM for Compliance and Customer Experience". You build and ship AI-based capabilities into the azakaw platform. This is a delivery role, not a research role. We are looking for someone who works fluently with AI-assisted development and can turn a business problem into working software in days rather than months, while holding the quality bar expected of a product used by regulated financial institutions.

The scope of the role follows the product roadmap and the priorities of the project. It is not limited to a fixed list of components, and you will be expected to work across the stack and across product areas. Indicative examples include AI capabilities for client onboarding, document review, risk analysis and compliance support; retrieval, reasoning and orchestration pipelines including agentic workflows and tool-calling patterns; document extraction and classification within identity verification and corporate data flows; and automation of client management processes across the platform, the CRM and internal systems.

Everything you ship is used by institutions under regulatory supervision. Accuracy, traceability and data protection are not optional refinements. They are part of the definition of done.

12-18 Month Mission Outcomes ("What Success Looks Like")

• AI Capabilities Live in Production AI-based capabilities delivered into the azakaw platform and used by clients or internal teams in production, not confined to prototypes. Every capability released with a documented evaluation set and an owner.
• Delivery Cadence Established Production releases at least weekly. Standard features move from agreed scope to production within 10 working days. 85% or more of committed cycle scope delivered.
• AI Output Trusted by Compliance Users Released AI features meet their documented accuracy threshold, with a minimum floor of 90% against the agreed evaluation set. Incorrect or unsupported outputs reaching end users below 2%. No repeat incidents from the same root cause.
• Measurable Reduction in Manual Effort At least two onboarding, review or compliance workflows with manual handling time reduced by 20% or more. Adoption of delivered features by target users at 70% or above within 60 days of release.
• AI-Assisted Development Embedded Repository rules, prompts, context files and agentic workflows documented and reused by the wider engineering team. Delivery approach repeatable by others, not dependent on one person.
• Project Commitments Met All deliverables, technical documentation and progress reports for the funded project submitted by the deadline. Effort records complete and accurate.

Key Accountabilities

1. Platform Development

Key Responsibilities: Design, build and ship features across the azakaw platform, working end to end from problem definition through to production and ongoing support. Work across the stack. Take features through code review, testing, release and post-release support.

Success Indicators: Weekly production releases. Lead time under 10 working days for standard features. 85%+ of committed scope delivered.

2. AI Capability Delivery

Key Responsibilities: Design and deliver AI-based capabilities applied to onboarding, document review, risk analysis, compliance support and client management. Build retrieval, reasoning and orchestration pipelines, including agentic workflows and tool-calling patterns. Integrate document extraction and classification into identity and corporate data flows.

Success Indicators: Capabilities in production use. Accuracy at or above the documented threshold before release. Latency within the limit defined per use case.

3. AI-Assisted Development Practice

Key Responsibilities: Use AI coding tools as the primary way of working, from prototype through to production code. Structure the prompts, context, repository rules and agentic workflows that make this reliable and repeatable. Review generated code critically before it ships.

Success Indicators: Practice documented and adopted by the wider team. Change failure rate at or below 15% despite high delivery velocity.

4. Evaluation, Monitoring and Cost

Key Responsibilities: Create and maintain evaluation sets and prompt regression tests covering accuracy, error rate, latency and cost. Monitor inference cost and usage. Optimise model selection, caching and context strategy. Implement guardrails, prompt versioning and observability.

Success Indicators: 100% of AI features ship with an evaluation set. Inference cost per processed case flat or declining as volume grows.

5. Security, Privacy and AI Governance

Key Responsibilities: Ensure solutions comply with GDPR, internal data protection policy and security requirements aligned with ISO 27001, with particular attention to data minimisation and the handling of personal data. Apply the group AI governance principles, including traceability of decisions and human oversight at critical points.

Success Indicators: No data protection findings attributable to delivered work. Traceability and human oversight evidenced for every decision-supporting feature.

6. Project Delivery and Reporting

Key Responsibilities: Contribute to the technical documentation, deliverables and progress reports required by the funded project. Meet defined milestones. Maintain accurate records of effort allocated to the project.

Success Indicators: 100% of project deliverables and reports submitted by deadline. Effort records submitted within 3 working days of period end.

7. Collaboration and Knowledge Sharing

Key Responsibilities: Work directly with compliance, product and client-facing teams to translate real regulatory and operational problems into technical solutions. Share techniques, tooling and lessons learned with the engineering team.

Success Indicators: Code review turnaround within 1 working day. Positive peer feedback. Knowledge sharing active and documented.

Core Competencies

• AI-Assisted Delivery: Demonstrated ability to deliver production software with AI coding tools. Knows where these tools accelerate work and where they introduce risk, and reviews generated code with the same rigour as their own. This is a working method, not a curiosity.
• Applied AI Engineering: Hands-on experience with large language model APIs, retrieval-augmented generation, embeddings, vector databases and tool calling. Understands context management, evaluation and failure modes. Chooses the simplest approach that solves the problem.
• Software Engineering Fundamentals: Strong command of Python and of TypeScript or JavaScript, with knowledge of modern web frameworks. Writes maintainable, tested code. Comfortable with SQL and relational databases, cloud environments, containers, Git and CI/CD pipelines.
• Product Judgement: Understands the business problem before writing code. Pushes back on requirements that do not survive contact with reality. Ships increments that deliver value rather than waiting for completeness.
• Evaluation and Measurement: Treats AI output as something to be measured, not assumed. Builds evaluation sets, tracks accuracy and cost over time, and can explain in numbers why a feature is ready to release.
• Data Protection Awareness: Understands GDPR obligations and the sensitivity of the personal and regulatory data the platform processes. Designs for data minimisation by default.
• Autonomy and Ownership: Operates without micromanagement in a distributed team. Sets their own priorities against agreed outcomes, raises blockers early, and closes the loop without being chased.
• Communication: Explains technical decisions and trade-offs clearly to non-technical stakeholders, including compliance and commercial teams. Documents what matters and skips what does not.
• English Language Proficiency: Fluent written and spoken English is a critical requirement for this role. English is the working language of the group, and all technical documentation, code review, project reporting and cross-office communication are produced in English. Candidates who cannot demonstrate strong English proficiency in both writing and conversation will not be progressed.

Education and Experience

Education

Completed bachelor’s degree (licenciatura, QNQ level 6) or higher in Computer Engineering, Computer Science, Data Science, Applied Mathematics, or a related field. A completed qualification at this level is a requirement for this position and will be verified before an offer is made.

Experience

Minimum 3 years building software that runs in production, including hands-on delivery of AI-based features. Concrete examples of systems built and shipped with AI coding tools are required and will be examined in detail during the selection process.

Preferred

Experience in RegTech, fintech, banking or compliance, particularly KYC, KYB, AML or sanctions screening. Experience with CRM platforms and commercial process automation. Knowledge of OCR and computer vision applied to identity documents. Experience with fine-tuning, distillation or self-hosted open-weight models. Open source contributions or a personal portfolio of products built with AI.

Rewards and Growth

• Competitive salary and benefits package.
• A role in an AI project with defined scope, approved funding and direct impact on the product, inside a company that already serves regulated clients at scale.
• Paid access to leading AI tooling, with no artificial usage limits.
• Hybrid working model and flexible hours from the Porto engineering hub.
• Career progression into senior engineering or technical leadership tracks as the AI function scales.
• Training and certification budget with continuous professional development.
• Exposure to an international group operating across the UAE, Portugal, Pakistan, the Philippines, South Africa and Saudi Arabia.

Job Title

Artificial Intelligence Engineer - azakaw