AI ARCHITECT & ENGINEERING CONSULTANT

Join Our Team as an AI Architect & Engineering Consultant!

About Us:

We are at the forefront of AI innovation, dedicated to developing cutting-edge solutions that push the boundaries of technology. Our team is driven by a passion for excellence and a commitment to delivering impactful AI solutions.

Position: AI Architect & Engineering Consultant

Tasks & Responsibilities:

A. AI Architecture Services:

Pre-Project Technical Consulting

• Conduct comprehensive technical assessments to estimate the effort, complexity, and resource requirements for new AI products and features.

• Provide expert guidance on the feasibility, scalability, risks, and business impact of proposed AI solutions.

• Advise stakeholders on technology selection, architecture approaches, and implementation strategies.

AI Architecture Development

• Design, establish, and maintain a scalable and secure AI architecture framework that supports deployment across edge, cloud, and on-premises environments.

• Define reference architectures, design patterns, and integration frameworks to ensure consistency across AI products and platforms.

• Ensure seamless interoperability, lifecycle management, and version control of AI models, services, and products.

Framework and Standards Definition

• Define end-to-end processes for AI model development, including data preparation, training, validation, testing, deployment, monitoring, and operations within a microservices-based ecosystem.

• Establish and govern technical standards, architectural principles, and best practices to ensure quality, reliability, security, and maintainability.

• Develop governance frameworks covering AI engineering, model management, MLOps, observability, and AI service operations.

Service Integration and Interoperability

• Define and implement interaction patterns for AI microservices to ensure effective communication with enterprise applications and non-AI services.

• Design robust APIs, interfaces, and integration mechanisms that support scalable, secure, and high-performance service interactions.

• Ensure interoperability across platforms, systems, and technology domains.

Operational Excellence and Engineering Standards

• Identify opportunities to leverage common platforms, reusable components, and shared capabilities across AI products to improve efficiency and reduce duplication.

• Define, implement, and enforce coding standards, development practices, and quality controls across AI engineering teams.

• Drive continuous improvement of architecture, development methodologies, and operational processes.

Self-Service Enablement

• Develop platforms, tools, templates, and frameworks that enable teams to independently develop, deploy, and manage AI solutions.

• Promote engineering autonomy through reusable assets, automation, and self-service capabilities.

Technology Strategy and Innovation

• Continuously evaluate emerging technologies, industry trends, and best practices to advance the organisation's AI capabilities and innovation agenda.

• Recommend and drive adoption of new technologies that create strategic business value.

Cross-Functional Collaboration

• Partner closely with shared services teams, including infrastructure, cybersecurity, data, and enterprise architecture functions, to ensure alignment, governance, and operational support for AI initiatives.

• Act as the primary architectural authority for AI-related programmes and strategic technology decisions.

B. AI Engineering Services

Development Governance

• Provide technical leadership and governance across internal and external development teams delivering AI solutions.

• Resolve technical dependencies, architectural challenges, and delivery impediments to ensure successful project execution.

• Ensure adherence to established architectural standards, engineering principles, and development best practices.

Product and Technical Enablement

• Partner with Product Owners and business stakeholders to translate business requirements into scalable technical solutions.

• Provide technical direction and engineering expertise throughout the product lifecycle.

• Define technical enablers and architecture runway requirements to support product roadmap delivery.

Testing, Quality Assurance and Release Planning

• Define and implement appropriate testing strategies, including functional, performance, security, integration, and model validation testing.

• Ensure AI solutions meet required standards for reliability, scalability, accuracy, and operational performance.

• Contribute to technical planning, release management, and deployment activities while ensuring alignment with architectural principles.

Technical Consulting and Architecture Support

• Act as a trusted technical adviser to AI Architects and delivery teams, providing expertise on architecture decisions, implementation challenges, and technology choices.

• Evaluate technical requirements, identify risks, and recommend mitigation strategies to support successful delivery outcomes.

Agile Delivery and Team Development

• Foster a collaborative, self-organising, and high-performing engineering culture within Agile delivery teams.

• Participate in Agile ceremonies and promote engineering excellence, continuous improvement, and DevOps/MLOps practices.

• Mentor and develop engineering talent to strengthen organisational AI capabilities.

Shared Services Collaboration

• Maintain strong partnerships with shared services teams, including infrastructure, cybersecurity, enterprise platforms, and operations functions, to ensure seamless deployment, integration, and support of AI solutions.

• Facilitate effective coordination between technology teams to achieve successful and sustainable AI implementations.

Required Competencies:

• AI Design Patterns: Deep understanding of AI design patterns and their implementation.
• Data Processing: Expertise in data processing for AI microservices.
• Machine Learning & Generative AI: Strong technical understanding of machine learning, statistics, and generative AI.
• Software Development: Established knowledge of modern software development and microservice frameworks.
• AI Technology Landscape: In-depth knowledge of the AI technology landscape (Classical & Generative).
• IT Infrastructure: Knowledge of IT infrastructure and interfaces is a plus.

Technical Competencies:

• Comprehensive understanding of AI architecture principles, design patterns, and their practical application in enterprise-scale solutions.

• Strong expertise in data engineering and data processing architectures supporting AI and machine learning microservices, ensuring efficient, scalable, and reliable data flows.

• In-depth knowledge of machine learning techniques, statistical modelling, deep learning, and Generative AI technologies, including large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI frameworks.

• Strong proficiency in modern software engineering practices, including microservices architecture, API design, containerisation, CI/CD, DevOps, and MLOps methodologies.

• Solid understanding of the AI technology landscape, including emerging trends, best practices, governance frameworks, and advances in both traditional and Generative AI solutions.
• Experience with large-scale data platforms and distributed computing technologies, including Apache Spark (PySpark), Apache Kafka, Hadoop, and modern data processing frameworks.
• Deep knowledge of cloud-native AI platforms and services, particularly Microsoft Azure, including Azure Machine Learning, Azure OpenAI Service, Azure Kubernetes Service (AKS), Azure AI Foundry, and related data and integration services.

• Understanding of AI model lifecycle management, including model training, evaluation, deployment, monitoring, observability, and responsible AI practices.
• Knowledge of enterprise integration patterns, security principles, identity management, and governance requirements for AI-enabled systems.
• Familiarity with software quality assurance practices, including automated testing, performance optimisation, reliability engineering, and secure-by-design development approaches.

Education & Work Experience:

• Diploma, Degree or Master's degree in computer science, data science, engineering or an AI-related discipline.
• Minimum of 2 years of experience with a proven track record in implementing complex AI microservices frameworks.

Soft Skills:

• Curiosity: Independent mindset with a passion for exploring new technological and methodological options.
• Customer Focus: Active interest in understanding customer business needs and technological processes.
• Communication & Collaboration: Strong communication and collaboration skills with cross-functional thinking and acting.
• Innovation: Constantly rethink and improve our way of working for increased efficiency.
• Leadership: Ability to lead and coordinate project teams.

Why Join Us:

- Be part of a dynamic and innovative team shaping the future of AI.

- Work in an environment that fosters creativity and encourages continuous learning.

- Collaborate with experts and industry leaders in AI and technology.

- Enjoy a flexible and supportive work culture that values your input and growth.

If you are passionate about AI and have the skills and experience to drive our AI architecture to new heights, we would love to hear from you!

Apply now and become a key player in our AI journey!