Senior Data Engineer – AI & Agentic Systems

Virtual Connect Solutions; Madrid; On-site; EUR 90,000 - 130,000; Full time

Job summary

Virtual Connect Solutions is seeking a senior data/AI engineer to design and deliver GenAI and agentic AI systems from architecture through production. You will architect scalable data platforms and build robust batch and streaming pipelines tuned for modern AI workloads.

The role requires expert Python/SQL, experience with GenAI/LLMOps and cloud platforms, plus strong data governance skills. You will mentor engineers and collaborate with clients to turn business problems into technical

Qualifications

• A degree in Computer Science or Engineering, or equivalent experience.
• 5+ years in data engineering, including leading technical workstreams and mentoring engineers.
• Expert-level Python and SQL, plus comfort with Scala or Java when client systems need them.
• Hands-on experience building Agentic AI, GenAI, ML and BI systems in production with Spark, Databricks, LangChain, Dask, Airflow, Dagster or Kedro.
• Practical knowledge of RAG, embeddings, vector databases and prompt and context engineering.
• Strong MLOps/LLMOps skills: CI/CD for data workflows, automated agent evaluation, Terraform.
• Experience on at least one major cloud (AWS, Azure or GCP); multi-cloud is a plus.
• Proven experience with data security and governance (PII/PHI, authentication, RBAC).
• Excellent time management and ability to own workstreams.

Responsibilities

• Own technical workstreams. Design and deliver machine learning, Generative AI and agentic/autonomous AI systems from architecture through production.
• Architect scalable data platforms. Build robust batch and streaming pipelines for modern AI workloads.
• Integrate GenAI and agentic frameworks into client solutions, including RAG pipelines, embeddings and vector databases.
• Design MLOps/LLMOps architectures for model management, observability, evaluation and retraining.
• Lead feature engineering on complex workstreams so data assets are reliable and reusable.
• Govern and secure data with access controls, PII/PHI handling, authentication and RBAC.
• Balance quality, latency and cost; apply evaluation and guardrail practices to AI features.
• Mentor and raise the bar; coach junior engineers and promote best practices.
• Partner with clients and stakeholders to translate business problems into technical solutions.

Skills

Python

SQL

LangChain

MLOps

LLMOps

Terraform

Spark

Vector databases

Prompt design

Education

Degree in Computer Science or Engineering

Tools

Spark

Databricks

LangChain

Dask

Airflow

Dagster

Kedro

LangSmith

Langfuse

Opik

Terraform

Job description

• Own technical workstreams. Design and deliver machine learning, Generative AI and agentic/autonomous AI systems from architecture through production.
• Architect scalable data platforms. Build robust batch and streaming pipelines that are tuned for modern AI workloads.
• Integrate GenAI and agentic frameworks into client solutions. This covers RAG pipelines, embeddings, vector databases, context construction, prompt design and output handling.
• Design MLOps/LLMOps architectures for model management, observability, automated evaluation and retraining.
• Lead feature engineering on complex workstreams so that data assets are reliable, reusable and ready for AI.
• Govern and secure data. Implement access controls, PII/PHI handling, authentication and RBAC across cloud environments.
• Make sound engineering trade-offs between quality, latency and cost. Apply evaluation, reliability and guardrail practices to AI features.
• Mentor and raise the bar. Coach junior engineers and drive software engineering best practices across the team.
• Partner with clients and stakeholders. Turn business problems into technical solutions and help build client capabilities.
  

What You Will Do

• Own technical workstreams. Design and deliver machine learning, Generative AI and agentic/autonomous AI systems from architecture through production.
• Architect scalable data platforms. Build robust batch and streaming pipelines that are tuned for modern AI workloads.
• Integrate GenAI and agentic frameworks into client solutions. This covers RAG pipelines, embeddings, vector databases, context construction, prompt design and output handling.
• Design MLOps/LLMOps architectures for model management, observability, automated evaluation and retraining.
• Lead feature engineering on complex workstreams so that data assets are reliable, reusable and ready for AI.
• Govern and secure data. Implement access controls, PII/PHI handling, authentication and RBAC across cloud environments.
• Make sound engineering trade-offs between quality, latency and cost. Apply evaluation, reliability and guardrail practices to AI features.
• Mentor and raise the bar. Coach junior engineers and drive software engineering best practices across the team.
• Partner with clients and stakeholders. Turn business problems into technical solutions and help build client capabilities.
  

Must have

• A degree in Computer Science or Engineering, or equivalent experience
• 5+ years in data engineering, including leading technical workstreams and mentoring engineers
• Expert-level Python and SQL, plus comfort with Scala or Java when client systems need them
• Hands-on experience building Agentic AI, GenAI, ML and BI systems in production with frameworks such as Spark, Databricks, LangChain, Dask, Airflow, Dagster or Kedro
• Practical knowledge of RAG, embeddings, vector databases and prompt and context engineering
• Strong MLOps/LLMOps skills:
  • CI/CD for data workflows
  • Automated agent evaluation (LangSmith, Langfuse, Opik)
  • Infrastructure as code (Terraform)
• Experience on at least one major cloud (AWS, Azure or GCP); multi-cloud is a plus
• Proven experience with data security and governance (PII/PHI, authentication, RBAC)
• The ability to own workstreams on your own, with excellent time management
• Clear, confident written and verbal communication in English

Skills: python,mlops,terraform