Lead GenAI Engineer with GCP

• 5–7 years of professional experience in software engineering, data engineering, machine learning or solution architecture.
• At least 3 years of practical experience delivering Generative AI solutions in production contexts.
• Proven experience designing and delivering solutions on Google Cloud Platform (GCP).
• Strong proficiency in Python and experience building production-grade backend services.
• Hands-on experience with LLM-based applications, including retrieval-augmented generation, AI agents and tool integration, prompt and context management, structured outputs, and evaluation and monitoring of AI solutions.
• Experience designing and integrating REST APIs, microservices and event-driven architectures.
• Practical knowledge of CI/CD, automated testing and deployment processes using Azure DevOps or equivalent tooling.
• Experience with containerised workloads, preferably using Docker and Kubernetes or Azure Container Apps.
• Ability to make and clearly communicate architectural decisions to technical and non-technical stakeholders.
• Experience working directly with international clients and distributed delivery teams.
• English language skills at minimum B2+/C1 level.

Nice to have:

• Familiarity with Vertex AI or comparable cloud AI services.
• Knowledge of frameworks such as LlamaIndex, LangChain or LangGraph.
• Experience with enterprise authentication and authorization, including Microsoft Entra ID and managed identities.
• Understanding of MLOps/LLMOps practices, model evaluation and application observability.
• Experience integrating AI capabilities with Microsoft 365, Microsoft Teams or other enterprise platforms.
• Familiarity with privacy, security and regulatory considerations related to AI solutions.
• Experience supporting presales activities, technical workshops or solution estimations.
• Relevant cloud or AI certifications.

• Design end-to-end architecture for Generative AI and agentic AI solutions, ensuring scalability, security and maintainability.
• Translate business and functional requirements into robust technical designs and integration patterns for AI components, APIs, data sources and enterprise applications.
• Lead technical delivery from discovery and proof of concept through production deployment, including design and code reviews.
• Provide technical direction, mentor less experienced engineers and establish engineering standards for solution quality, observability and performance.
• Identify technical risks, dependencies and architectural trade-offs, and recommend pragmatic alternatives.
• Support effort estimation, delivery planning and decision-making during project phases.
• Evaluate AI frameworks, models and cloud services to align with project requirements and enterprise constraints.
• Ensure solutions adhere to responsible AI principles, data protection and enterprise security expectations.
• Collaborate with client stakeholders, architects, product owners, data engineers and software development teams, including distributed and international teams.