Principal Solution Architect - Agentic Ai Platform

Intellias; Granada; Presencial; EUR 120.000 - 180.000; Jornada completa

Descripción de la vacante

Intellias busca un Principal Solution Architect para liderar el diseño y evolución de plataformas Agentic AI empresariales. Definirá patrones de arquitectura, gobernanza, orquestación e integración para sistemas AI de producción basados en agentes autónomos, LLMs, herramientas y servicios empresariales.

El rol abarca colaborar con múltiples dominios de plataforma, garantizar escalabilidad, seguridad y observabilidad conforme a gobernanza empresarial, en un contexto remoto desde España.

Formación

• Arquitectura de sistemas Agentic AI para entornos empresariales.
• Experiencia con al menos un framework de orquestación de agentes en producción.
• Diseño de integraciones agente‑a‑herramienta y agente‑a‑agente a nivel de plataforma.

Responsabilidades

• Diseñar e implementar marcos de evaluación automatizados para flujos LangGraph y pipelines de orquestación.
• Desarrollar suites de evaluación en tiempo de compilación para comportamiento de agentes y calidad de razonamiento.
• Crear metodologías de evaluación que combinen puntuación determinista y LLM como juez.
• Construir harness de pruebas para grafos LangGraph y rutas de ejecución de agentes.
• Definir métricas de fiabilidad y gobernanza para lanzamientos de agentes.

Conocimientos

Agentic AI system architecture

Agent orchestration frameworks

Agent‑to‑tool integration

LLM application architecture

Agent governance and runtime controls

Herramientas

LangGraph

Strands

AutoGen

CrewAI

AWS Bedrock AgentCore

Descripción del empleo

Principal Solution Architect - Agentic AI Platform Intellias•Granada, andalucía, Spain

Descripción del trabajo

Principal Solution Architect - Agentic AI Platform

Location: Remote from Spain (an indefinite Spanish employment contract)

We are looking for a Principal Solution Architect to lead the design and evolution of enterprise-scale Agentic AI platforms. In this role, you will define architecture patterns, governance models, orchestration strategies, and integration standards for production AI systems built around autonomous agents, LLMs, tools, and enterprise services. You will work across multiple platform domains to ensure agentic solutions are scalable, secure, observable, and aligned with enterprise governance requirements.

Project Overview:

Our customer is a multinational corporation with more than a century of history and offices in over 180 countries. Their most ambitious goal at the time is to introduce a range of Reduced-Risk Products (RRPs). The target audience is more than 1 billion consumers around the globe. IT platform hosts 700+ applications.

Intellia's mission is to help the client with the engineering of a comprehensive software ecosystem for a game-changing IoT product on the margin of innovative consumer experience and cutting‑edge technology. Our teams are involved in the engineering of core platform components for best‑in‑class eCommerce, Digital Marketing and IoT solutions. As an Engineer, you will become a part of Core Architecture Team and be responsible for the architecture, implementation of best practices in our Digital Engineering Enterprise Platform.

The Platform is a set of services and internet applications that accelerate the development and delivery of software applications by taking care of common SDLC challenges. The Platform provides access and consumption for engineering teams to a set of services, technologies, practices for their development and for operating their application, ensuring a set of compliance and best practices.

Requirements:

Skills:

• Agentic AI system architecture (agent orchestration, multi‑agent delegation, tool use, memory/state management)
• Agent orchestration frameworks (LangGraph, Strands, AutoGen, CrewAI, or comparable - production‑level depth in at least one)
• Agent‑to‑tool and agent‑to‑agent integration patterns (function calling, MCP, A2A, or equivalent protocols)
• LLM application architecture (RAG pipelines, prompt/context engineering, model routing, evaluation of LLM outputs)
• Agent governance and runtime controls (authorization/policy enforcement for autonomous actions, identity for non‑human actors)

Experience:

• 6+ years software architecture experience, including 2+ years specifically architecting LLM‑powered or agentic AI systems in production
• Hands‑on production experience with at least one agent orchestration framework (LangGraph, Strands, AutoGen, CrewAI, or equivalent), owning architecture decisions, not just implementation
• Experience designing agent‑to‑tool integration at a platform level (a tool/integration layer consumed by multiple agents or teams, not a single agent's custom tool wiring)
• Experience architecting at least two of the following for agentic systems: runtime/execution environment, tool/gateway integration, identity and policy enforcement, observability and evaluation
• Demonstrated experience presenting architecture for governance or technical review (ARB, design review board, or equivalent)

Nice to have:

• Hands‑on experience with AWS Bedrock AgentCore (Runtime, Gateway, Registry, Policy, Evaluation, Observability)
• Experience with policy‑as‑code frameworks for agent authorization (Cedar, OPA, or similar)
• Familiarity with MCP and A2A protocol specifications
• Prior architecture role spanning multiple platform domains in an enterprise, regulated, or highly governed environment
• Experience with agent identity standards (e.g., non‑human identity federation, MS Entra Agent ID or equivalent)
• Observability and evaluation architecture specific to agentic systems (tracing multi‑step agent reasoning, quality/safety monitoring)
• Cloud architecture supporting managed agent runtimes (AWS preferred - networking, IAM, managed compute)

Responsibilities:

• Design and implement automated evaluation frameworks for LangGraph‑based agent workflows and orchestration pipelines.
• Develop build‑time evaluation suites covering agent behavior, tool selection accuracy, reasoning quality, and final output quality.
• Create evaluation methodologies combining deterministic grading approaches with LLM‑as‑judge evaluation techniques.
• Build test harnesses for LangGraph graphs, nodes, state transitions, and agent execution paths.
• Design multi‑turn conversation simulations to validate context retention, memory utilization, and workflow consistency.
• Define and maintain reliability metrics, including and pass^k methodologies, to measure both success rates and behavioral consistency.
• Implement CI/CD deployment gates that automatically block releases when evaluation thresholds are not met.
• Develop validation processes for staging environments, shadow‑mode comparisons, and controlled production rollouts.
• Integrate AWS AgentCore Evaluations (on‑demand and online evaluation modes) into continuous testing and quality assurance workflows.
• Transform production incidents, failures, and unexpected agent behavior into regression test cases.
• Establish evaluation metrics, quality benchmarks, and acceptance criteria for agent releases.
• Collaborate with AI engineers, platform teams, and product stakeholders to improve agent reliability and performance.
• Define observability and feedback mechanisms that connect production behavior with test framework improvements. xkdbapo
• Produce documentation for evaluation strategies, scoring methodologies, deployment gates, and quality standards.