AI Solution Architect at Saab designing and deploying scalable AI systems in the defence sector. Leading delivery of AI solutions and collaborating with cross-functional teams.
Responsibilities
Design and architect scalable, secure, and reliable AI infrastructure using microservices, API gateways, and serverless architecture patterns
Lead the end-to-end delivery of AI solutions from requirements and system design through to deployment and monitoring in production environments
Build and maintain CI/CD pipelines, containerised deployments (Docker, Kubernetes), and model tracking workflows using e.g. MLflow
Develop and expose AI capabilities through robust API services, integrating ML models into existing platforms and enterprise systems
Apply hands-on expertise in ML/DL frameworks
Leverage cloud AI services across AWS and/or Microsoft Azure to architect cost-effective and performant solutions
Manage and process large-scale data using Spark or Kafka, ensuring robust data engineering practices
Ensure data governance, privacy compliance and security best practices are embedded into every solution
Align AI initiatives with business strategy, contributing to vendor selection, cost optimisation, and technology roadmap decisions
Communicate complex AI architectures and trade-offs clearly to both technical teams and executive stakeholders
Collaborate cross-functionally with data scientists, ML engineers, solution owners, and domain experts to deliver outcomes that matter
Requirements
3–7 years of experience in AI, ML, or data science roles
A minimum of a Bachelor's degree in Computer Science, Data Science, Software Engineering, Mathematics, or a related field
Strong proficiency in Python; solid working knowledge of Java and/or C++ for high-performance applications
Practical experience with ML/DL frameworks: TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers
Proven MLOps skills including CI/CD pipeline design, Docker, Kubernetes, model tracking, and API development
Experience with large-scale data engineering tools such as Apache Spark or Kafka
Solid understanding of cloud AI platforms (AWS, Azure) and associated AI/ML services
Knowledge of data governance principles and compliance with privacy regulations
Excellent communication skills
Strong project management abilities and a track record of effective stakeholder management
Outcome-oriented mindset with a focus on delivering measurable business value
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