AI Software Engineer | Spain
Madrid, Torre Chamartin • Full-time
Posted 1mo ago
About the job
You will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. This role involves working directly with client engineering teams, leading technical design sessions, and building reusable patterns and accelerators that scale beyond individual engagements. You will focus on building systems that make AI work in enterprise environments, not just demos or prototypes, with a strong emphasis on shipped multi-agent systems in production, owning the evaluation harness, and understanding agent failure scenarios. This position offers broad exposure across industries, enterprise technology stacks, and organizational complexities, with opportunities for vendor fellowship access and a direct pathway to a Forward Deployed Engineer program.
Responsibilities
- Design and build production-grade agentic systems, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
- Build and own RAG pipelines, covering embeddings, chunking strategy, vector search, and context window engineering.
- Integrate and abstract across multiple LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) with fallback routing, token, cost, and latency management.
- Implement LLMOps in production, including eval harnesses with real quality metrics, prompt versioning, observability tooling, and cost/safety monitoring.
- Embed with client engineering teams to design, prototype, and deploy agentic solutions through workshops, proofs of concept, and code-with sessions.
- Build reusable patterns, accelerators, and playbooks to scale solutions and enable faster starts for future engagements.
- Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness, presenting findings to client stakeholders.
Requirements
- Strong software engineering experience in production environments.
- Hands-on experience designing and deploying agentic AI solutions in a production environment.
- Demonstrated experience with agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen at a production depth.
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction and token/latency/cost management.
- Experience with RAG pipeline ownership, including embeddings, chunking strategy, vector databases, and context engineering.
- Understanding of LLMOps fundamentals: eval harness design, prompt versioning, and production observability.
- Cloud-native engineering maturity, including Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
- Strong Python skills; Java or equivalent backend language is acceptable.
- Production debugging and observability experience.
- Quality of experience is weighted over years; preference for candidates who have shipped multiple production agentic systems.