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Senior AI Engineer
Verity Group. Design, develop, and evolve agents and workflows using MCP-based architectures .
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Design, develop, and evolve agents and workflows using MCP-based architectures
- Build RAG pipelines and context-retrieval mechanisms using vector databases
- Integrate agents with APIs, tools, and external services to execute complex workflows
- Develop agents in Python, preferably using Google ADK
- Build and maintain agentic workflows using DeepAgents
- Integrate AI models and services through Vertex AI and Gemini models
- Define architectural 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 ready for continuous evolution
- Define and apply validation and evaluation strategies for LLMs and agents, using metrics and tools such as DeepEval and LLM-as-a-Judge approaches
Requirements
What you’ll need- Strong experience with Python development
- Hands-on experience building agents, intelligent workflows, and integrations with generative AI models
- Experience with DeepAgents
- Knowledge of MCP-based architectures
- Experience with RAG pipelines and vector databases
- Experience integrating APIs, tools, and external services
- Experience with Vertex AI and Gemini models
- Ability to define technical standards, reusable abstractions, and engineering best practices
- Experience with observability, including logs, metrics, and tracing
- Knowledge of LLM and agent validation and evaluation, including tools such as DeepEval and LLM-as-a-Judge approaches
- Ability to collaborate effectively with engineering, architecture, and technical squad teams
- Systems-level perspective for understanding data flows, integrations, and enterprise environment constraints