Forward Deployed Engineer

6001 TECNILOGICA ECOS. Company; Málaga; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Accenture is seeking a senior engineer to architect and govern production-grade agentic AI systems at enterprise scale in a client-focused environment. You will lead multi-LLM integration, define RAG standards, and drive observable, cost-aware LLMOps practices across multiple deployments.

You will engage with client technology leaders, guide PoC deliveries, and publish scalable patterns and patterns that accelerate new engagements.

Formación

• Production environments experience required.
• Hands-on experience designing and deploying agentic AI solutions in production.
• Experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen in production depth.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, production observability.
• Cloud-native engineering: Kubernetes, Docker, microservices, serverless, CI/CD, IaC (Terraform/Helm).
• Strong Python; Java or equivalent backend language; production debugging & observability.

Responsabilidades

• Architect and govern production-grade agentic systems at enterprise scale across environments.
• Define RAG pipeline standards and quality benchmarks with transferable documentation.
• Set multi-LLM integration standards with vendor-agnostic architecture and cost governance.
• Own LLMOps at programme scale including eval strategy, prompt governance, and observability tooling.
• Lead client engineering engagements: architecture sessions, PoC delivery, alignment with client tech leadership.
• Publish reusable patterns, accelerators, and engineering standards for scalable adoption.
• Own the measurement framework for agentic system quality and present AI impact to senior stakeholders.

Conocimientos

Production-grade systems

LLMOps

Python/Java

Kubernetes Docker

Cloud-native

LLM APIs

Herramientas

LangGraph

CrewAI

AutoGen

Descripción del empleo

Key Responsibilities

• Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability


• Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable


• Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models


• Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems


• Lead client engineering engagements at senior level — facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams


• Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements


• Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders

Qualifications

• Software engineering experience in production environments


• Hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable


• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level


• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs


• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering


• LLMOps fundamentals: eval harness design, prompt versioning, and production observability


• Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)


• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience


• Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure


• People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at www.accenture.com

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