Forward Deployed Agile Software Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Forward Deployed Agile Software Engineer based in Spain. Join a forward-thinking engineering environment where software development, artificial intelligence, and product innovation come together to solve complex business challenges. In this role, you will work directly with clients to transform ambiguous needs into practical, production-ready digital solutions. You will design and build AI-powered applications, autonomous agents, intelligent workflows, and full-stack software tailored to real-world business requirements. Beyond developing models and prompts, you will engineer the systems that make AI applications reliable, secure, observable, and scalable. Collaborating with multidisciplinary teams, you will move seamlessly from technical discovery and rapid prototyping to deployment and continuous improvement. You will have the opportunity to influence technical strategy, establish engineering best practices, and deliver measurable value across diverse industries. This role is ideal for a hands-on engineer who thrives in dynamic environments, enjoys customer collaboration, and is passionate about bringing emerging AI technologies into production. Accountabilities - Client collaboration and technical discovery: Partner with clients and internal teams to understand business objectives, operational workflows, technical environments, data sources, and constraints. Translate ambiguous challenges into clear requirements, solution architectures, prototypes, and actionable delivery plans. - AI application and software development: Design, develop, and deploy AI-enabled applications, agentic workflows, APIs, developer tools, data pipelines, and full-stack products that address real business needs.

- AI harness engineering: Build the infrastructure surrounding AI models, including prompts, tools, context management, memory, permissions, runtime controls, observability, and feedback mechanisms required for reliable production performance. - Agent orchestration and workflow design: Develop and refine agent loops that support planning, tool selection, execution, observation, reflection, recovery, and termination. Model complex workflows using graphs, state machines, and orchestration patterns that coordinate models, systems, data, and human approvals. - Evaluation and quality assurance: Create evaluation frameworks and testing strategies to measure task completion, accuracy, reliability, safety, latency, cost, and user value. Use representative datasets, regression testing, human review, and production feedback to continuously improve system performance. - Rapid prototyping and product delivery: Build proofs of concept, validate solutions with real users, and transform successful prototypes into secure, scalable, maintainable production systems. - Enterprise integration: Connect AI solutions to client systems, enterprise data sources, cloud platforms, APIs, databases, and third-party services while ensuring compatibility with existing technology environments. - AI safety, security, and reliability: Establish appropriate safeguards, permission boundaries, approval mechanisms, and fallback strategies for autonomous and semi-autonomous systems. Identify and address issues such as hallucinations, prompt injection, context loss, looping, brittle tool calls, and unsafe execution. - Observability and troubleshooting: Instrument applications to capture traces, intermediate states, performance metrics, and failures. Diagnose complex technical and operational issues and use insights to improve reliability and user outcomes. - Engineering excellence: Apply strong software engineering practices across automated testing, documentation, security, version control, observability, and continuous integration and delivery.