[ASI] AI Solutions Architect

Software Mind develops solutions impacting companies globally, working with tech giants and unicorns on transformative projects involving emerging technologies.

• 5+ years of experience in IT architecture, solutions architecture, or senior engineering roles OR 2+ years in AI/ML architecture
• Proven experience designing and delivering AI/ML or Generative AI solutions in production
• Strong understanding of LLMs, prompt engineering, RAG, and agentic architectures
• Hands-on or strong understanding of frameworks such as LangChain, LlamaIndex, or similar
• Experience architecting solutions on cloud platforms (AWS, Azure, GCP)
• Strong knowledge of API design, system integration, and distributed architectures
• Experience with ML/AIOps practices (deployment, monitoring, lifecycle management)
• Proven ability to engage stakeholders and translate business needs into technical solutions
• Excellent communication skills in English and Polish

Nice to have:

• Experience in IT pre-sales, consulting, or client-facing solution design roles
• Familiarity with agent-based systems and AI orchestration frameworks
• Experience with fine-tuning, evaluation, and optimization of LLMs
• Knowledge of Docker, Kubernetes, and scalable deployment patterns
• Understanding of security, compliance, and responsible AI principles
• Strong presentation and storytelling skills for technical and non-technical audiences

• Design end-to-end architecture for AI-driven solutions focusing on Generative AI (LLMs, RAG, agents, automation)
• Engage in AI pre-sales activities: translate business requirements into AI solution proposals and architectures, prepare architecture diagrams, conduct client workshops, discovery sessions, and technical presentations
• Design scalable, secure, and cost-efficient AI architectures on cloud platforms (AWS, Azure, GCP)
• Define and oversee implementation of AI solutions such as RAG pipelines, vector search, intelligent systems, agents, and LLM integrations
• Provide technical leadership and guidance to engineering teams during delivery
• Follow best practices for AI system design, governance, and responsible AI