Solutions Architect

Intellias; Portugal; Vor Ort; EUR 120.000 - 180.000; Vollzeit

Zusammenfassung

Intellias seeks a Principal Engineer to shape AI-driven software delivery, setting engineering strategy, architecture standards, and enterprise AI governance across the lifecycle. This leader guides teams in AI-native practices with emphasis on security, quality, traceability, and compliance.

The project focuses on building scalable, automated AI platforms for a global enterprise. You will influence architecture decisions, mentor engineers, and drive innovation in AI orchestration,

Qualifikationen

• Bachelor's or Master's degree in Computer Science, Engineering, AI, or equivalent experience.
• 12+ years of software engineering experience.
• 5+ years leading architecture or platform engineering initiatives.
• Deep expertise in cloud-native architecture and distributed systems.
• Strong understanding of modern AI/ML technologies, LLMs, agent frameworks, RAG, vector databases, and MLOps.
• Strong experience in enterprise architecture and AI engineering platforms, including LLM orchestration, RAG, and knowledge systems.
• Solid expertise in cloud-native architectures, microservices, event-driven systems, Kubernetes, CI/CD, and DevSecOps across Azure, AWS, or GCP.
• Experience defining and implementing AI governance frameworks and enterprise technology standards.
• Strong strategic technical leadership, systems thinking, and an innovation-driven approach.
• Excellent executive communication and ability to influence technology decisions across the enterprise.
• Proven experience in mentoring and coaching technical teams and driving architectural alignment.

Aufgaben

• Define enterprise architecture and engineering standards for AI-enabled development.
• Create frameworks, reference architectures, and reusable platform capabilities.
• Act as the ultimate escalation point for complex technical decisions.
• Lead adoption of AI-assisted software engineering practices.
• Establish standards for prompt engineering, agent-based development, AI testing, AI observability, and AI governance.
• Drive AI integration throughout the software development lifecycle.
• Champion Domain-Driven Design (DDD), Behaviour-Driven Development (BDD), and Test-Driven Development (TDD).
• Define best practices for automated code generation, review, validation, and deployment.
• Establish engineering metrics and quality gates.
• Ensure responsible AI implementation.
• Define controls around security, privacy, bias, explainability, and compliance.
• Collaborate with architecture, security, legal, and governance teams.
• Mentor senior engineers and technical leads.
• Drive engineering capability development and innovation.
• Represent the organization in executive technical discussions.

Kenntnisse

Cloud-native architecture

Kubernetes

CI/CD

DevSecOps

LLMs / AI engineering

AI governance

enterprise architecture

Executive communication

Mentoring

Strategic leadership

Ausbildung

Bachelor's/Master's in CS/Engineering/AI

Tools

Azure

AWS

GCP

LLM orchestration

RAG & knowledge systems

Jobbeschreibung

The Principal Engineer is the foremost technical authority for AI-driven software delivery practices. The role defines engineering strategy, architecture standards, AI governance implementation, and enterprise-scale adoption of AI throughout the software development lifecycle. This individual drives transformation towards AI-native engineering practices while ensuring security, quality, traceability, and compliance. Inspired by emerging AI-DLC practices, the Principal Engineer guides teams in leveraging AI across requirements, design, development, testing, deployment, and operations

Our client is a large global enterprise with a complex technology landscape and a strong focus on digital transformation and innovation. The organisation is investing in modern cloud, data, and AI capabilities to build scalable, secure, and automated solutions across its business.

The project focuses on building an enterprise-grade AI Agent Platform from the ground up, providing a standardised foundation for developing, deploying, orchestrating, securing, and observing AI agents. The initiative brings together AI orchestration, observability, security, governance, integrations, evaluation, and platform engineering, giving engineers the opportunity to shape key architectural decisions and build a new platform from the ground up.

Responsibilities

• Bachelor's or Master's degree in Computer Science, Engineering, AI, or equivalent experience.
• 12+ years of software engineering experience.
• 5+ years leading architecture or platform engineering initiatives.
• Deep expertise in cloud-native architecture and distributed systems.
• Strong understanding of modern AI/ML technologies, LLMs, agent frameworks, RAG, vector databases, and MLOps.
• Strong experience in enterprise architecture and AI engineering platforms, including LLM orchestration, RAG, and knowledge systems.
• Solid expertise in cloud-native architectures, microservices, event-driven systems, Kubernetes, CI/CD, and DevSecOps across Azure, AWS, or GCP.
• Experience defining and implementing AI governance frameworks and enterprise technology standards.
• Strong strategic technical leadership, systems thinking, and an innovation-driven approach.
• Excellent executive communication and ability to influence technology decisions across the enterprise.
• Proven experience in mentoring and coaching technical teams and driving architectural alignment.

Responsibilities

• Define enterprise architecture and engineering standards for AI-enabled development.
• Create frameworks, reference architectures, and reusable platform capabilities.
• Act as the ultimate escalation point for complex technical decisions.
• Lead adoption of AI-assisted software engineering practices.
• Establish standards for prompt engineering, agent-based development, AI testing, AI observability, and AI governance.
• Drive AI integration throughout the software development lifecycle.
• Champion Domain-Driven Design (DDD), Behaviour-Driven Development (BDD), and Test-Driven Development (TDD).
• Define best practices for automated code generation, review, validation, and deployment.
• Establish engineering metrics and quality gates.
• Ensure responsible AI implementation.
• Define controls around security, privacy, bias, explainability, and compliance.
• Collaborate with architecture, security, legal, and governance teams.
• Mentor senior engineers and technical leads.
• Drive engineering capability development and innovation.
• Represent the organization in executive technical discussions.