5 months ago
Valencia, SpainSenior
Responsibilities
- Embed with clients to understand workflows, identify underlying business problems, and redesign functions from first principles.
- Design, build, optimize, and deploy production GenAI, LLM-based, agentic, backend, and data pipeline systems.
- Implement production RAG systems and build evaluation harnesses with defined success criteria, ground truth, and release gates.
- Develop multi-step agent workflows with orchestration, tool use, state management, and recovery from partial failure.
- Deploy maintainable systems on AWS or other required cloud platforms using containers, CI/CD, automated testing, monitoring, and operational handoff.
- Lead architecture reviews, write technical design documents, contribute to engineering standards, and mentor engineers.
- Own technical direction for proposals and scoping, including delivery plans, dependencies, risks, build costs, and run costs.
- Present outcomes to senior client stakeholders, support adoption and change management, and feed field learnings back into reusable blueprints.
Requirements
- 7+ years of experience building and running production systems.
- Strong AI/ML foundations and the ability to reason about model failure modes.
- Experience designing and shipping production LLM applications and agentic workflows, not only demos, proofs of concept, or notebooks.
- Experience with LLM APIs such as Anthropic, AWS Bedrock, or OpenAI and with agent frameworks.
- Production experience building and optimizing RAG systems, evaluation suites, model and agent monitoring, and drift detection.
- Python and/or TypeScript proficiency, strong engineering fundamentals, and the ability to work productively in unfamiliar codebases or languages.
- Hands-on AWS production experience with services such as Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, and ECR; GCP or Azure experience is a plus.
- Experience with containers, ECS or Kubernetes, infrastructure as code, and CI/CD for AI pipelines.
- Experience with production Claude ecosystem tools including Claude Code, CLAUDE.md, hooks, and skills files.
- Knowledge of MCP and the ability to explain its advantages over REST integrations; authoring an MCP server is a plus.
- Ability to make and defend architectural trade-offs, control model cost and latency, and work with model tiering and caching.
- B2+ English proficiency and comfort collaborating across distributed, multicultural teams.
- Ability to engage credibly with business leaders, CTOs, engineers, and other senior stakeholders.
- Preferred qualifications include founder, CTO, or engineering leadership experience; financial services, insurance, or healthcare experience; consulting or embedded delivery experience; A2A knowledge; AWS or Claude Code certifications; GitHub Actions or GitLab CI experience; Go, TypeScript, or Rust experience; and Apache Spark, Apache Airflow, or Kafka experience.
Benefits
- Remote-friendly culture.
- Internal training and support for Claude, AWS, and other professional certifications, plus conference attendance.
- Career growth and active engineering development.
- Access to the latest AI tools and premium subscriptions.
- Long-term B2B collaboration.
- Private medical insurance or a budget for medical needs.
- Paid sick leave, vacation, and public holidays.
- Equipment and technology needed for productive work.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureGitHub ActionsGitLab CI/CDGoGoogle Cloud PlatformKubernetesPythonRustTypeScript
Categories
Forward Deployed
