IA/ML Engineer
Datadope · Spain
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- Hybrid
- Full-time
- $120,000 / year
- Spain
Job highlights
- Build intelligent AI/ML solutions for complex systems.
- Develop and deploy ML models and AI agents.
- Implement autonomous agents with LLMs.
- Integrate LLMs with internal data (RAG).
- Collaborate on production pipelines and MLOps.
About the role
AI / ML Engineer at DataDope
Join DataDope as an AI / ML Engineer and build intelligent solutions that combine data, models, and autonomous agents applied to complex observability and network systems. If you enjoy designing AI systems integrating Machine Learning, LLMs, and real-time operational data, developing intelligent agents to analyze events, alerts, and metrics, and deploying them robustly and scalably, we want you on our team!
What you will do at DataDope:
- Transform business challenges into end-to-end AI / ML solutions, deeply understanding business objectives.
- Design, develop, and deploy Machine Learning models and AI agent workflows for anomaly analysis, prediction, classification, and correlation of events, networks, and user experience.
- Implement autonomous agents and decision systems using frameworks like LangGraph, CrewAI, OpenAI, and AutoGen.
- Develop LLM-based agents capable of analyzing operational events, alerts, metrics, and logs.
- Design automated reasoning systems to help explain incidents or recommend actions on observability platforms.
- Integrate LLMs with internal data (RAG) using vector databases like Chroma or Pinecone.
- Collaborate with Data Engineers to design reproducible and scalable training and inference pipelines (Spark, Airflow, MLflow).
- Develop backend components, MCPs, and APIs (Python / FastAPI) to expose models or agents to products.
- Create and maintain reproducible experimentation environments (Docker, conda/pipenv, Jupyter).
- Document experiments, metrics, hyperparameters, and validation results with traceability and quality.
- Collaborate closely with Data Science, Data Engineering, and DevOps/MLOps teams to integrate models into production pipelines.
- (Advanced Plus) Design hybrid architectures combining traditional ML, generative AI, and structured knowledge (Graph / RAG).
- Collaborate with the team and guide junior profiles, sharing knowledge and best practices.
What we need from you:
- Education in Computer Science, Mathematics, Engineering, or quantitative disciplines.
- Experience in developing and implementing Machine Learning or AI models.
- Solid Python experience, with expert handling of Pandas and NumPy.
- Proficiency in scikit-learn, StatsModels, and Deep Learning frameworks (TensorFlow, PyTorch).
- Real-world experience in time series forecasting, beyond basic statistical models (plus).
- Autonomy and team spirit, as we like to learn together at DataDope.
- Experience in training, tuning, and deploying ML models (supervised and unsupervised).
- Hands-on experience with agent and LLM orchestration frameworks (LangGraph, CrewAI, AutoGen).
- Experience with vector databases and RAG techniques (Retrieval-Augmented Generation).
- Experience with data pipelines and training.
- Ability to analyze model results and metrics.
- Skill in documenting and communicating technical results clearly and structuredly.
If you also have:
- Knowledge of cloud platforms (AWS, GCP, Azure).
- Experience with NoSQL databases (MongoDB, ClickHouse) and technologies like Kafka.
- Experience with generative AI and open-source LLMs (OpenAI, Anthropic, Mistral, Ollama, Hugging Face).
- Knowledge of Graph Databases and Knowledge Graphs.
- Familiarity with multi-role or multi-modal agent topologies.
- Knowledge in model optimization (quantization, distillation, LoRA fine-tuning).
- Experience in monitoring and continuous evaluation of models in production (Prometheus, EvidentlyAI, Arize).
- Knowledge in MLOps: MLflow, DVC, Docker, CI/CD, model versioning, and experiment tracking.
We offer:
- A collaborative, inclusive, and innovation-oriented work environment.
- Exciting projects that will challenge your skills and allow you to grow professionally.
- Leadership and personal growth opportunities.
- Competitive salary commensurate with your experience and the role's responsibilities.
- Flexible working hours with a remote model (with the possibility of hybrid if you reside in Madrid).
- Reduced working hours on Fridays, July, and August.
- 25 days of vacation + your birthday off.
- Flexible benefits package.
Interested?
If you feel this role is for you and you want to be part of the team redefining the future of observability, we would love to hear from you! You can send your CV directly to rrhh@datadope.io
Join DataDope and lead the observability revolution!
Key skills/competency:
- AI/ML Engineer
- Machine Learning
- LLMs
- Autonomous Agents
- Observability
- Python
- Deep Learning
- Data Pipelines
- MLOps
- Cloud Platforms
Skills & topics
- AI Engineer
- ML Engineer
- Machine Learning
- Python
- LLM
- Autonomous Agents
- Data Observability
- Deep Learning
- RAG
- MLOps
How to get hired
- Tailor your resume: Highlight AI/ML, Python, LLM, and agent framework experience.
- Showcase projects: Detail end-to-end AI/ML solutions, model deployment, and MLOps.
- Prepare for technical questions: Review ML algorithms, deep learning, RAG, and agent implementation.
- Demonstrate collaboration: Provide examples of teamwork with Data Engineers and DevOps/MLOps.
- Highlight problem-solving: Explain how you translate business needs into AI/ML solutions.
Technical preparation
Master Python, Pandas, NumPy, scikit-learn.,Practice deep learning frameworks (TF, PyTorch).,Build agent systems with LangGraph, CrewAI.,Implement RAG with vector databases.
Behavioral questions
Describe a complex AI/ML problem you solved.,How do you translate business needs to AI solutions?,Share an experience mentoring junior team members.,How do you ensure model traceability and documentation?
Frequently asked questions
- What are the primary responsibilities of an AI/ML Engineer at DataDope?
- As an AI/ML Engineer at DataDope, you will be responsible for designing, developing, and deploying end-to-end AI/ML solutions. This includes building ML models, implementing autonomous agents using LLMs and frameworks like LangGraph, integrating LLMs with internal data via RAG, and collaborating with engineering teams on production pipelines and MLOps.
- What technical skills are essential for the AI/ML Engineer role at DataDope?
- Essential technical skills include a strong foundation in Python with expertise in Pandas and NumPy, proficiency in scikit-learn and deep learning frameworks (TensorFlow, PyTorch), hands-on experience with agent frameworks (LangGraph, CrewAI, AutoGen), vector databases, RAG techniques, and a solid understanding of data pipelines and ML model training, tuning, and deployment.
- Does DataDope offer remote work for AI/ML Engineer positions?
- Yes, DataDope offers a remote work model for its AI/ML Engineer positions, with the possibility of a hybrid arrangement for candidates residing in Madrid. This allows for flexibility in how and where you work.
- What kind of projects can an AI/ML Engineer expect to work on at DataDope?
- You can expect to work on exciting projects that involve building intelligent systems for observability and networks. This includes developing AI solutions for anomaly detection, prediction, classification, event correlation, and creating automated reasoning systems to explain incidents or recommend actions.
- How can I apply for the AI/ML Engineer job at DataDope?
- To apply for the AI/ML Engineer position at DataDope, you can send your CV directly to the provided email address: rrhh@datadope.io. Make sure your CV highlights your relevant AI/ML and Python experience.
- What is the preferred educational background for a DataDope AI/ML Engineer?
- DataDope prefers candidates with a background in Computer Science, Mathematics, Engineering, or other quantitative disciplines. This provides a strong foundation for the technical challenges involved in AI and Machine Learning.
- Are there opportunities for growth and leadership in this AI/ML Engineer role?
- Absolutely. DataDope offers opportunities for leadership and personal growth. You'll have the chance to collaborate with the team, guide junior profiles, and advance your career within an innovative and collaborative environment.