Solution Architect AI

PKO Finat Sp. z o.o. is seeking a Solution Architect AI to deliver architecture and implementations of AI solutions including LLM/GenAI and RAG. The role involves working with cloud AI services such as GCP Vertex AI, Azure OpenAI, and AWS Bedrock, and developing a multitenant AI platform.

• Higher education in computer science, mathematics, machine learning, data science, or related fields.
• Minimum 5 years of experience designing and implementing AI solutions, including at least 2 years with LLM and GenAI.
• Documented experience with cloud AI services: GCP Vertex AI or Azure OpenAI / AWS Bedrock.
• Practical knowledge of GCP Vertex AI (models, pipelines, endpoints, Model Registry).
• Ability to design LLM and RAG solutions (LangChain, LlamaIndex, or equivalents).
• Experience in machine learning (modeling, evaluation, deployment).
• Experience creating architectural documentation (HLD, LLD).
• Knowledge of MLOps (pipelines, model monitoring, CI/CD for ML).
• Understanding of AI governance and regulations (EU AI Act, RODO).
• Strong knowledge of microservices architecture, API management, and modern integration patterns.
• Experience in cybersecurity architecture, data architecture, and infrastructure design.
• English language proficiency at B2 level or higher (technical documentation, RFCs, vendor communication).

Nice to have:

• Knowledge of open source frameworks: Hugging Face, LlamaIndex, LangGraph.
• Experience with embedding models and vector databases.
• GCP Professional Machine Learning Engineer or Professional Cloud Architect certification.
• AI/ML certifications (e.g., DeepLearning.AI, Hugging Face).

• Deliver architecture and implementations of AI solutions including LLM/GenAI and RAG.
• Utilize cloud AI services (GCP Vertex AI, Azure OpenAI, AWS Bedrock) and work practically with Vertex AI components (models, pipelines, endpoints, Model Registry).
• Develop and maintain a multitenant AI platform, including workflows for Agents and RAG, and CI/CD for configurations and deployments.
• Define integration approaches: microservices architecture, API management, and integration patterns.
• Co-shape cybersecurity architecture, data architecture, and infrastructure design for AI solutions and platform.
• Apply MLOps practices: pipelines, model monitoring, and CI/CD for ML.
• Build and maintain architectural documentation (HLD/LLD) and communicate solutions to technical and business stakeholders in a cross-functional environment.
• Ensure AI governance and regulatory compliance (EU AI Act, RODO).