Forward Deployed Engineer (AI)

Location: Remote, Madrid, Spain

Company

hirify.global builds AI-native enterprise software for asset management, operations, and critical services, combining LLMs, agentic AI, and cloud-native technologies.

What you will do

• Embed with product teams to identify business problems where AI can provide meaningful value.
• Lead technical discovery, distinguish process bottlenecks from symptoms, and translate business needs into technical solutions.
• Reuse or extend existing AI services and help teams design and build AI-first applications.
• Gather requirements and partner with AI engineering teams to scope, design, and build new services when existing capabilities do not fit.
• Work hands-on with data pipelines, model training and serving, MLOps tooling, and product integrations.
• Ship initial versions quickly, then add monitoring, feedback loops, reliability, and roadmap input based on real usage.

Requirements

• 5+ years of software engineering experience, including hands-on experience building and deploying production systems and working across unfamiliar stacks.
• Strong Python skills and the ability and willingness to work with other languages and existing customer codebases.
• Experience building and operating cloud-native services; Azure experience with AKS, Blob Storage, Key Vault, and Azure-hosted AI or model services is highly relevant.
• Production integration of LLMs and AI services, including model APIs, authentication, gateways, reliability, latency, cost, and observability.
• Experience building agentic applications with tools, APIs, and orchestration frameworks; hands-on MCP experience is strongly preferred.
• Hands-on experience designing production RAG and retrieval systems, including embeddings, vector databases, indexing, retrieval quality, and evaluation.
• Strong experience with REST APIs, authentication and authorization, external systems, data contracts, and complex integrations.
• Experience with Docker, Kubernetes, Helm, GitOps or ArgoCD, and Terraform or equivalent infrastructure-as-code tooling.
• Experience with data pipelines and relational, document, vector, and object stores; understanding of event-driven and distributed systems architecture.
• Understanding of AI evaluation, observability, monitoring, failure modes, and quality, latency, reliability, and cost trade-offs.
• Experience building and owning production ML or data systems, not only notebooks or prototypes, and experience working in or alongside platform or infrastructure teams.
• Ability to work through ambiguity, rapidly prototype, productionize solutions, and communicate technical trade-offs clearly to non-technical stakeholders.

Nice to have

• Hands-on MCP experience.
• Azure experience, particularly with AKS, Blob Storage, Key Vault, and Azure-hosted AI or model services.

Culture & Benefits

• Remote workplace type with flexibility and hybrid work opportunities.
• International environment with colleagues and customers across all continents.
• Inclusive culture focused on collaboration, trust, sustainability, and positive impact.
• Opportunity to work on production AI systems at enterprise scale.