AI Engineer AI Engineer (AI Platform & Data Engineering) Location: Remote, Spain Candidates must be based in Spain and have the existing right to work in Spain. We are unable to offer visa sponsorship or relocation support for this role Salary: EUR 60,000–70,000 per annum (depending on experience)

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Overview of Position

Speedcast delivers highly customizable, mission-critical communication solutions for remote sites worldwide. Our Data Engineering team operates a modern lakehouse platform — Kafka streaming, Iceberg on S3, a cloud query layer, and time-series databases on AWS — and we are building AI-powered products and internal tools on top of it. As an AI Engineer on the platform side, you will build and operate the infrastructure that makes AI applications reliable in production: the data pipelines that feed them, the embedding and retrieval layers behind them, and the services, APIs, and monitoring around them. This role carries a strong data engineering component — you will cross-function with the wider team on platform duties, contributing to ingestion pipelines, data quality, and operational excellence across our data systems. The successful candidate is a strong software and data engineer who is excited by the LLM application stack, cares about reliability, security, and cost, and is capable of working independently while collaborating closely with colleagues across Data Engineering, Data Science, and DevOps.

What you will do

- Design, build, and operate the pipelines that power AI applications: document and data ingestion, chunking and embedding pipelines, vector stores, and retrieval services.




- Productionize LLM-powered applications as reliable services: APIs, containerised deployments, observability, cost monitoring, and access control.
- Integrate AI workloads with the data lakehouse platform — streaming (Kafka) and batch sources, query layers, and time-series stores.
- Build and maintain evaluation, logging, and feedback infrastructure for AI applications in production.
- Contribute to core data engineering duties: developing and maintaining data pipelines, ensuring data quality and reliability, and improving platform performance and efficiency.
- Implement security, privacy, and governance best practices for AI systems handling company data.
- Collaborate with data scientists, AI engineers, software engineers, and business stakeholders to deliver AI-driven solutions.
- Participate in Agile ceremonies, backlog grooming, sprint planning, and prioritisation activities.
- Evaluate and support the adoption of new AI platform technologies, tools, and architectural approaches.

What we need from you
- 3–5 years of experience in software engineering or data engineering, with hands‑on exposure to AI/LLM application development.
- Commercial experience with Python and strong SQL skills (any major SQL dialect).
- Experience developing and maintaining data pipelines in production.
- Experience integrating foundation model APIs (e.g., Amazon Bedrock,



Anthropic, OpenAI) into applications or services.
- Hands‑on experience with AWS services (e.g., Lambda, Step Functions, S3, ECR, Glue) and AWS security best practices.
- Experience containerising applications using Docker.
- Experience building and operating APIs and backend services.
- Understanding of data quality, data governance, and data engineering best practices.
- Experience using Git for source control and working within Agile/Scrum environments.
- Strong analytical and problem‑solving skills, and excellent communication and stakeholder management skills.
- Even better if you have Experience with streaming platforms: Confluent Kafka, Avro/Schema Registry, Kafka Connect, or Apache Flink.
- Experience with lakehouse architectures: Apache Iceberg, Parquet, medallion patterns, and dbt.
- Experience with query engines such as Dremio and time‑series databases such as TimescaleDB.
- Experience with vector search (pgvector or dedicated vector databases) and retrieval infrastructure at scale.
- Experience with agentic frameworks, tool use, or the Model Context Protocol (MCP).
- Experience with Infrastructure as Code (e.g., CloudFormation, Terraform) and CI/CD pipelines.
- Experience with cost monitoring and optimisation of cloud and AI workloads.
- Good understanding of GDPR and data governance principles.
- Advanced AWS expertise, including relevant AWS certifications.

Working at Speedcast

At Speedcast, we want people that like adventure. Who are motivated by work that takes uncommon ingenuity and thinking outside the box. Who relish the idea of working in an industry undergoing massive transformation. If you want to be part

📌 AI Engineer (España)
🏢 Hermes Datacommunications International
📍 España