AI Architect

Що робити

Solution architecture. Design and own end-to-end AI systems for strategic enterprise clients - reference architectures, data and retrieval pipelines, agentic layers, and integration with existing enterprise systems - with a clear path from prototype to scalable, secure, observable production.
Pre-sales & consulting. Partner with sales to qualify and win AI opportunities: run technical discovery, scope solutions, size effort and cost, and translate AI into business value, ROI, and risk for C-level stakeholders.
Technical leadership & mentorship. Set architecture and engineering standards, run design and code reviews, and level up engineers across the AI practice.
AI Lab & R&D. Stand up and run the internal AI Lab - rapid client prototypes and cost-effective internal AI tooling - with a clear bar for what graduates into production.
Partner collaboration. Work with our core AI partners (Google Cloud / Vertex AI, Anthropic) to build joint solutions, access early capabilities, and shape our technical roadmap.

Experience & seniority. 5+ years in software, data, or ML engineering, including 3+ years delivering ML/AI in production and 2+ years in a client-facing architect or technical lead capacity.
Cloud & AI infrastructure. Production-level experience on Google Cloud Platform, especially the Vertex AI suite, plus integrating third-party model providers such as Anthropic.
GenAI engineering. Strong hands-on knowledge of RAG, agentic workflows, orchestration frameworks (LangChain, LlamaIndex), vector databases, and prompt/context engineering.
Production & MLOps. Hands-on with the ML lifecycle, CI/CD for models, automated deployment, and cloud cost management - including right-sizing models (lighter vs. heavier) to control spend.
Evaluation, guardrails & observability. Practical experience building LLM evaluations, guardrails, and monitoring for accuracy, safety, latency, and cost in production.