ML Engineer (MLOps - Remote)
Codesearch AI
Boiro, Spain
2 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Remote
Boiro, Spain
Tech stack
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Cloud Computing
DevOps
Monitoring of Systems
Python
TensorFlow
Prometheus
Management of Software Versions
Google Cloud Platform
Cloud Platform System
PyTorch
Large Language Models
Grafana
Generative AI
Kubernetes
Information Technology
HuggingFace
Machine Learning Operations
Stable Diffusion
GPT
Software Version Control
Data Pipelines
Docker
Databricks
Job description
- Production-Ready GenAI Infrastructure: Design and deploy scalable MLOps pipelines specifically optimized for GenAI applications and large language models
- State-of-the-Art Model Deployment: Implement and fine-tune advanced models like GPT and similar architectures in production environments
- Hybrid AI Systems: Create solutions that integrate traditional ML techniques with cutting-edge LLMs to deliver powerful insights
- Automated MLOps Workflows: Build robust CI/CD pipelines for ML, enabling seamless testing, validation, and deployment
- Cost-Efficient Cloud Infrastructure: Optimize cloud resources to maximize performance while maintaining cost efficiency
- Governance and Versioning Systems: Establish best practices for model versioning, reproducibility, and responsible AI deployment
- Integrated Data Pipelines: Utilize Databricks to construct and manage sophisticated data and ML pipelines
- Monitoring Ecosystems: Implement comprehensive monitoring systems to ensure reliability and performance
Requirements
- 4+ years of hands-on experience in MLOps, DevOps, or ML Engineering roles
- Experience with MLflow, DVC, Prometheus, and Grafana for versioning and monitoring
- Proven expertise deploying and scaling Generative AI models (GPT, Stable Diffusion, BERT)
- Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Hugging Face)
- Strong cloud platform experience (AWS, GCP, Azure) and managed AI/ML services
- Practical experience with Docker, Kubernetes, and container orchestration
- Databricks expertise, including ML workflows and data pipeline integration
- Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience)
- Fluency in written and spoken English
The Person We're Looking For
- You're a builder at heart - someone who loves creating scalable, production-ready systems
- You balance technical excellence with pragmatic delivery
- You're excited about pushing boundaries in GenAI and LLM technologies
- You can communicate complex concepts effectively to diverse stakeholders
- You enjoy mentoring junior team members and elevating the entire technical organization, Location - this is a remote opportunity, but the successful candidate must reside permanently in Spain and not require sponsorship
About the company
Our client is a globally recognised brand with deep-rooted expertise. They are heavily invested in leveraging AI to combine their domain expertise with SOTA techniques, solidifying their position as a leader in the field. You'll join a global team with a distributed set of skills including Research, Applied AI and Engineering.
They are seeking MLOps Engineers to help architect the future of communication through AI. This isn't just another engineering role - it's an opportunity to pioneer systems that transform how companies connect with their customers
What You'll Be Doing
You'll be designing and optimising production-grade MLOps pipelines that bring cutting-edge Generative AI and LLMs from experimentation to real-world impact. Your expertise will directly influence how some of the world's leading brands enhance their strategies., You'll be working with a modern data stack designed to process large-scale information, automate analysis pipelines, and integrate seamlessly with AI-driven workflows. This is your chance to make a significant impact on projects that push the boundaries of AI-powered insights and automation in industry.