MLOps Engineer

TensorOps is an applied machine learning studio helping organizations worldwide plan, design, train, and deploy production-grade ML systems. Our clients range from NASDAQ-listed enterprises to seed-stage startups. Projects span from small proofs-of-concept to multi-year strategic initiatives.

• ML Infrastructure at Scale: Building and optimizing ML pipelines across cloud platforms
• Generative AI Deployment: Production-ready chatbots, agents, and LLM applications
• Traditional ML Systems: Time series forecasting, AdTech, computer vision in production
• Platform Engineering: CI/CD for ML, model serving infrastructure, and observability systems

As we work with many clients, our stack varies, but we often use:

Cloud Platforms (Primary Focus):

• GCP: Vertex AI, Cloud Run, GKE, BigQuery, Cloud Storage, Cloud Build
• AWS: SageMaker, Bedrock, EKS, S3, Lambda, Step Functions, ECR

Infrastructure & Orchestration:

• IaC: Terraform, CloudFormation
• Containers: Docker, Kubernetes (EKS, GKE)
• Workflow Orchestration: Airflow, Kubeflow Pipelines, Vertex AI Pipelines, SageMaker Pipelines

ML Tools & Frameworks:

• Model Training: PyTorch, HuggingFace, LightGBM, CatBoost
• Model Serving: FastAPI, TorchServe, TensorFlow Serving
• LLM Frameworks: LangChain, LangGraph

Observability & Monitoring:

• MLFlow, Weights & Biases, Langfuse
• Cloud-native monitoring (CloudWatch, Cloud Monitoring)
• Prometheus, Grafana

Data Engineering:

• Pandas, Polars, DuckDB
• BigQuery, Redshift, Athena

We're looking for an MLOps Engineer to help us build and scale ML infrastructure for our diverse client base. You'll report to and be mentored by a senior team member while working on cloud-native ML systems that serve real users. This is a hands-on role from day one, where you'll architect pipelines, automate deployments, and ensure reliability at scale.

• BSc in Computer Science, Software Engineering, or equivalent practical experience
• Demonstrable experience with GCP and/or AWS in production environments

• Cloud Expertise: Strong working knowledge of GCP and AWS ML/AI services (Vertex AI, SageMaker, Bedrock, etc.)
• DevOps Fundamentals: CI/CD pipelines, infrastructure-as-code (Terraform preferred), containerization
• MLOps Practices: Experience designing and maintaining ML pipelines, model versioning, automated retraining
• Python Proficiency: Strong Python skills with focus on production-ready code
• System Design: Understanding of distributed systems, scalability patterns, and reliability engineering
• Excellent English communication skills

• Kubernetes expertise (EKS, GKE administration)
• Experience with model monitoring and observability platforms
• Knowledge of LLM deployment patterns (RAG systems, agent architectures)
• Contributions to ML infrastructure tooling or open-source projects
• Multi-cloud architecture experience
• Certifications: GCP Professional ML Engineer, AWS Machine Learning Specialty, or CKA

• Fully remote (legal residence in Spain required)
• Real-world infrastructure challenges with immediate impact
• Work across cutting-edge cloud technologies and ML frameworks
• Mentorship from engineers who have built ML platforms at scale
• Competitive compensation with growth tied to ownership and performance rather than periodic reviews (which we still do)

• Yearly salary: €50,000-65,000 (adjusted for MLOps focus)
• Travel expenses allowance
• Urban Sports Club membership
• Professional development budget for certifications and training