Design, develop, and evolve agents and workflows using MCP-based architectures
Build RAG pipelines and context retrieval mechanisms leveraging vector databases
Integrate agents with external APIs, tools, and services to execute complex flows
Develop agents in Python using Google ADK
Build and maintain agentive flows with LangChain and LangGraph
Integrate AI models and services via Vertex AI and Gemini models
Implement build, test, and deployment pipelines for agents and workflows
Define architecture standards, reusable components, and technical governance guidelines for the platform
Ensure solution observability through logs, metrics, and tracing
Partner with technical teams to ensure consistent integration between AI agents and enterprise applications
Design solutions aligned with business processes, integrations, and objectives
Ensure developed components are scalable, reusable, and prepared for continuous evolution

Strong experience with Python development
Hands-on experience building agents, intelligent workflows, and integrations with generative AI models
Experience with LangChain and LangGraph
Knowledge of MCP-based architectures
Experience with RAG pipelines and vector databases
Experience integrating external APIs, tools, and services
Experience with Vertex AI and Gemini models
Familiarity with build, test, and deployment practices for applications
Ability to define technical standards, reusable abstractions, and engineering best practices
Experience with observability, including logs, metrics, and tracing
Ability to collaborate effectively with engineering, architecture teams, and technical squads
System-level thinking to understand data flows, integrations, and constraints of enterprise environments

Senior AI Engineer

Key skills