AI Data Architect

What is the project, and why should you care?

Symphony Solutions is a Cloud and AI-driven IT company headquartered in the Netherlands. We are a premier software provider of custom iGaming, Healthcare, and Airline solutions. Devoted to delivering the highest quality of service, we offer our expertise in full-cycle software development, cloud engineering, data and analytics, AI services, digital marketing orchestration, and more. Since our founding in 2008, Symphony Solutions has been serving many international clients primarily in Western Europe and North America.   

The AI Data Architect is a key technical player in the presales process, delivering new projects and developing service and engineering practices across all Symphony Solutions projects. AI Data Architect reports directly to VP Delivery. They work closely with other SA and with Service Delivery Managers and Product Owners. 

You will be an excellent fit for this position if you have:

• 7+ years of experience in software development with any of the following: Java, C#, Python, Go 
• Experience in leading and mentoring one or more software development teams 
• Experience with Lean / Agile / Scrum and team scaling practices and frameworks (SAFe, Less, or other) 
• At least one year of experience in solution architecture functions in service or product companies 
• Building trusted relationships with clients / internal customers 
• Influencing senior decision-makers 
• Analysis of business, product, and technology requirements, and architecture trade-off 

Excellent knowledge in Computer Science / Programming Theory: 

• Databases 
• Architectural Styles & Enterprise Patterns 
• Design patterns 
• Deployment patterns 
• Security patterns, computer security, and networking vulnerability 

Practical experience with: 

• High-load systems design 
• Cloud providers (AWS, Azure, GCP) 
• Microservices Architecture (Designing modular, scalable systems) 
• RDBMS / NoSQL DBs 
• Event-Driven Architecture (Kafka, RabbitMQ, Cloud Provider’s messaging services) 
• CI/CD pipeline core principles and tools (BitBucket/Gitlab CI Pipelines, Argo), deployment strategies 
• Containerisation and Orchestration, Kubernetes principles, Hybrid Cloud 
• Security posture & Data Privacy/Data Protection in software development 

Core AI & Data Knowledge: 

• Machine Learning Fundamentals: Supervised/unsupervised learning, model evaluation, overfitting/underfitting 
• Deep Learning Basics: Neural networks, CNNs, RNNs, Transformers (at least at a conceptual level) 
• Model Lifecycle Understanding: Training, validation, deployment, and monitoring of AI models 
• Cloud AI Services: Knowledge of AI offerings in AWS (SageMaker, Bedrock), Azure (Cognitive Services), or GCP (Vertex AI) 
• Familiarity with tools like Kubeflow, MLflow, SageMaker, TensorFlow Serving, or TorchServe 

AI & Data Systems Design & Architecture: 

• Data Engineering & Pipelines Designing robust data ingestion, ETL/ELT pipelines (e.g., Apache Airflow, Spark, Kafka) 
• MLOps & Deployment Pipelines 
• Gen AI LLMs landscape, SaaS vs self-managed, trade-off for choosing a model 
• Security in AI solutions (Understanding of secure model handling, data anonymisation, compliance) 
• Agentic AI Architecture Design, frameworks and tools for AI agents archestration (MCP, A2A, Google Agent Space, Azure AutoGen) 

Desirable: 

• Experience in Pre-Sales 
• Experience working with US/European clients 
• Certifications on (but not limited) Architecture frameworks, Cloud providers, Architect or Engineer badges, Scrum or scaling frameworks 

Interpersonal Skills: 

• Emotional intelligence 
• Critical thinking and analytical skills 
• Teamwork and collaboration 
• Effective communication and presentation skills 

Here are some of the things you’ll be working on:

Mission: 

• Lead technical pre-sales activities, including discovery sessions, client workshops, and RFP/RFI responses 
• Stakeholders management, customer consulting, requirements gathering, solutions trade-off, and effective communication 
• Extend the company’s technical competence in AI and Data analytics and build technical competence matrices 
• Participate in designing architecture and guiding development teams for both internal and external AI/DA projects, and contribute to building relevant case studies 
• Contribute to and evolve architects, AI, and data engineers’ technical communities 

Results: 

• Lead internal Agentic AI project to cover the most critical daily functions of multiple departments at the company and present the first agents’ end of Q3 
• Improve existing and add new capabilities, technical solutions, and expertise to Data Analytics and AI service lines, help to define the value proposition, and give technical input for sales and marketing teams 
• Efficiently participate in pre-sales activities related to AI, Data Analytics, and cloud projects, design high-level architectures, define assumptions/risk statements, drive estimation sessions, and present proposals to potential clients 
• Perform and present at least one PoCs for the latest AI and DA technologies per quarter 
• Conduct at least one technical workshop and lecture about technical expertise and knowledge obtained on projects per quarter 
• Build a technical competence matrix for AI and Data Analytics during the Q3/Q4 period 2025 
• Conduct on-request technical and cultural interviews for key technical roles associated with AI, Data Analytics, Software Architecture, and System Design