Senior Agentic AI Engineer
Overview
As a Senior Agentic AI Engineer, you design and deploy agentic AI and GenAI solutions for clients, guiding architecture from definition to production. You will lead project phases, ensure model quality and scalability, and establish evaluation strategies for reliable LLM-based systems. You’ll integrate agents with client ecosystems, collaborate with DevOps, and set engineering standards to maintain high code quality. This role offers technical leadership, client interaction, and opportunities to shape cutting-edge AI deployments at scale.
Retribuzione / Benefits salary defined by experience and skills (RAL 37,000–65,000)
structured career path
focus on growth opportunities (technical and managerial)
inclusive work environment
support for reasonable adjustments
wellbeing and work-life balance
Responsabilità Design and optimize agentic AI and GenAI solutions from architecture to production
Translate client business goals into actionable technical requirements
Lead project phases ensuring model quality and production readiness
Define evaluation strategies for LLM-based systems to ensure reliable behavior
Integrate agents with client systems and third-party services, collaborating with DevOps
Serve as a technical guide, establishing engineering standards and conducting code reviews
Requisiti fondamentali Bachelor's or Master's degree in Computer Science, Computer Engineering or related field
4–5 years of software development or ML/AI experience
1–2 years of experience on LLM-based projects and agentic solutions in production
Consulting or client-facing experience
Proficiency in Python and agentic frameworks (LangGraph, Semantic Kernel, AutoGen) with multi-agent design
Experience in prompt engineering, LLM evaluation, and RAG architectures
Knowledge of security measures for LLM systems and guardrails
API design (REST, SSE) and integration of agents with external systems via function calling
Knowledge of cloud AI services, scalability, resilience, and observability
Client listening and needs analysis
Ability to manage expectations, dependencies, blockers, and communicate risks
Autonomy and ownership in decision-making
Python
LangGraph
Semantic Kernel
As a Senior Agentic AI Engineer, you design and deploy agentic AI and GenAI solutions for clients, guiding architecture from definition to production. You will lead project phases, ensure model quality and scalability, and establish evaluation strategies for reliable LLM-based systems. You’ll integrate agents with client ecosystems, collaborate with DevOps, and set engineering standards to maintain high code quality. This role offers technical leadership, client interaction, and opportunities to shape cutting-edge AI deployments at scale.
Retribuzione / Benefits salary defined by experience and skills (RAL 37,000–65,000)
structured career path
focus on growth opportunities (technical and managerial)
inclusive work environment
support for reasonable adjustments
wellbeing and work-life balance
Responsabilità Design and optimize agentic AI and GenAI solutions from architecture to production
Translate client business goals into actionable technical requirements
Lead project phases ensuring model quality and production readiness
Define evaluation strategies for LLM-based systems to ensure reliable behavior
Integrate agents with client systems and third-party services, collaborating with DevOps
Serve as a technical guide, establishing engineering standards and conducting code reviews
Requisiti fondamentali Bachelor's or Master's degree in Computer Science, Computer Engineering or related field
4–5 years of software development or ML/AI experience
1–2 years of experience on LLM-based projects and agentic solutions in production
Consulting or client-facing experience
Proficiency in Python and agentic frameworks (LangGraph, Semantic Kernel, AutoGen) with multi-agent design
Experience in prompt engineering, LLM evaluation, and RAG architectures
Knowledge of security measures for LLM systems and guardrails
API design (REST, SSE) and integration of agents with external systems via function calling
Knowledge of cloud AI services, scalability, resilience, and observability
Client listening and needs analysis
Ability to manage expectations, dependencies, blockers, and communicate risks
Autonomy and ownership in decision-making
Python
LangGraph
Semantic Kernel