Lead Solution Architect - Data and AI

As a Lead Solution Architect - Data and AI at Capco Poland, you'll be responsible for designing end-to-end architectures for data processing and analytics platforms, optimizing data lakes and warehouses, and collaborating with various teams to deliver tailor-fit solutions. Your role will also include developing AI/ML deployment strategies and ensuring compliance with data security standards. Essentially, you'll serve as a bridge between business requirements and technology solutions, making a significant impact on project outcomes.

To qualify for the Lead Solution Architect - Data and AI role at Capco, candidates should hold a Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. Additionally, at least 8 years of solution architecture experience with a strong focus on data and AI/ML projects is essential. Proficiency in cloud platforms like Azure, AWS, or Google Cloud and familiarity with big-data tools and programming languages such as Python or Java is also critical.

In your role as Lead Solution Architect - Data and AI at Capco Poland, you will work with a wide range of technologies tailored to your projects. This includes cloud platforms like Azure and AWS, big data tools such as Spark and Hadoop, as well as analytics platforms like Power BI and Tableau. Experience with AI/ML frameworks like TensorFlow or PyTorch, and a solid grounding in programming will be pivotal for your success.

Capco is committed to fostering professional growth for its employees. As a Lead Solution Architect - Data and AI, you will have access to over 3,000 business courses, enabling you to acquire new skills or deepen your expertise. The culture at Capco emphasizes ongoing learning and innovation, ensuring you stay ahead of industry trends and threats while collaborating with senior partners and clients.

The work culture at Capco Poland is centered around innovation, collaboration, and inclusivity. As a Lead Solution Architect - Data and AI, you will be part of a diverse team that operates within a flat, non-hierarchical structure, allowing for direct interaction with senior partners and stakeholders. This culture empowers you to contribute more meaningfully and fosters an environment of shared growth.

When answering this question, detail specific projects where you've designed data architectures and implemented AI solutions. Discuss the technologies used, your role in the projects, and how your decisions impacted outcomes. Highlight any challenges faced and how you overcame them.

Explain your decision-making process for selecting technologies, emphasizing criteria like project requirements, team expertise, cost considerations, and scalability. You can provide examples of past experiences where your selections led to successful project implementations.

Discuss your hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud, detailing specific tasks you’ve performed and advantages you’ve gained from using these services. Showcase any relevant certifications or projects that demonstrate your expertise.

Share your strategies for ensuring compliance with data regulations, highlighting your understanding of GDPR and other laws. Discuss measures you’ve taken to maintain data security, quality, and lineage within your projects.

Emphasize your communication skills and ability to work effectively with diverse teams. Provide examples of successful collaborations, roles you played, and how cross-team cooperation resulted in successful project outcomes.

Outline your systematic approach to identifying bottlenecks or inefficiencies in data pipelines. Discuss specific methodologies and tools you’ve employed to refine these processes for better performance and scalability.

Share your experience with MLOps and the steps you take to operationalize AI/ML models, including model training, deployment, and monitoring. Highlight frameworks you've worked with and any challenges you've overcome in the operationalization process.

Reflect on a specific example where you explained complex technical ideas to non-technical stakeholders. Explain how you adapted your communication style and the impact of that communication on project understanding and decision-making.

Discuss the resources, courses, or communities you engage with to ensure you stay current with trends in AI and data technology. Highlight your commitment to self-improvement and practical applications of your learning in your work.

Demonstrate your dedication to mentoring by sharing strategies you employ to guide junior team members. Discuss how you foster a supportive learning environment and your approach to providing constructive feedback.