Integrate Generative AI models, such as LLMs, with external APIs, tools, and databases using secure and efficient orchestration patterns.
Design, develop, and deploy AI workflows and Agentic AI solutions, enabling the seamless orchestration of intelligent agents to plan and perform tasks while leveraging autonomous and/or human-in-the-loop paradigms.
Implement and optimize multi-agent systems, leveraging standards and protocols such as Model Context Protocol (MCP), and emerging frameworks for agent interoperability and access to external resources.
Develop evaluation frameworks, metrics, and checkpoints for agent autonomy, performance, and safety, ensuring compliance with moderation, security, and ethical standards.
Evaluate, analyse, and gather insights out of structured and non-structure data leveraging Generative AI models and pipelines.
Ensure robust AI agent operations by applying observability, monitoring, and MLOps best practices, facilitating reliable deployment pipelines and continuous performance optimization.
Orchestrate AI model selection, tuning, and performance validation to meet specific agent-based application needs.
Communicate complex AI concepts, systems, and decisions effectively to technical and non-technical stakeholders, promoting transparency and trust in AI delivery.
Foster an environment of innovation and collaboration, engaging and encouraging teams to solve complex problems and share ideas that drive innovative approaches.

Proven experience designing and scaling complex AI systems, with strong expertise in Generative AI, LLM architectures, and distributed systems.
Strong background in software and platform architecture, with experience designing API-driven, modular, and extensible systems.
Deep understanding of orchestration patterns for LLMs, including tool-calling, RAG pipelines, memory systems, and agent coordination frameworks.
Experience architecting or implementing agentic AI systems using frameworks such as LangGraph, OpenAI Agents SDK, CrewAI, Google ADK, or similar.
Strong knowledge of data architecture for AI, including embeddings, vector databases, chunking strategies, and retrieval optimization.
Experience with evaluation frameworks and observability tools (e.g., RAGAS, OpenAI Eval, Arize, LangSmith, Braintrust), and defining system-level metrics.
Hands-on experience with cloud platforms (preferably GCP) and AI services (e.g., Vertex AI), with a focus on scalable and secure deployments.
Experience designing systems with CI/CD pipelines, monitoring, logging, and performance optimization at scale.
Strong understanding of security, governance, and responsible AI practices in production environments.
Ability to translate complex AI architectures into clear technical and business narratives for diverse stakeholders.

Senior Staff AI Engineer, Architect

Key skills