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Senior Machine Learning Engineer
hace 3 semanas
Descripción
We are looking for a Senior ML Engineer to take technical ownership of our machine learning production environment. You will lead the transition of experimental models into production-grade services that are reliable, scalable, and cost-effective. Your mission is to build the "highway" that allows our data science team to deploy models rapidly while ensuring those models are observable and fiscally responsible. You will own the entire ML lifecycle—from automated training pipelines to real-time inference clusters—and serve as a key software engineering contributor to our AI product stack.
This is a hybrid role – three days per week in our Barcelona or Newcastle office.
Responsabilidades principales
In this role your key responsibilities will be:
- Lifecycle & Pipeline Architecture: Design and own the automated "Continuous Training" (CT) and deployment pipelines. Architect reusable, modular infrastructure for model training and serving, ensuring the entire lifecycle is versioned and reproducible.
- Software Engineering Best Practices: Lead the team in adopting professional engineering standards. This includes owning the strategy for unit/integration testing, peer code reviews, and applying SOLID principles to ML codebases to ensure they remain modular and maintainable.
- ML Observability: Establish and own the telemetry framework for the AI stack. Implement proactive monitoring for system health and model-specific metrics, such as data drift, concept drift, and prediction accuracy.
- FinOps & Cost Management: Own the strategy for AI cloud spend. Build monitoring and alerting frameworks to track compute costs (training and inference) and implement optimization strategies like auto-scaling and spot-instance usage.
- AI Systems Engineering: Act as a lead software engineer to integrate models into the product ecosystem. Develop high-performance, secure APIs and microservices that wrap our ML capabilities for production consumption.
- Data & Model Governance: Own the versioning strategy for the "Holy Trinity" of ML: code, data, and model artifacts. Ensure clear documentation and audit trails for all production deployments.
What we're looking for:
Essential skills (entry requirements):
- Demonstrating strong software engineering fundamentals, including production‑quality Python, testing, CI/CD practices, and version control
- Designing and operating reliable, versioned REST APIs using an API‑first approach
- Building, deploying, and operating backend services in cloud environments, with AWS as the primary platform (experience on other major clouds considered transferable)
- Using containerisation and modern deployment approaches, including Docker, automated pipelines, and basic observability
- Working effectively with real‑world data and production systems in collaboration with product, data, and platform teams
- Bringing either hands‑on experience delivering machine‑learning systems in production or a very strong software‑engineering background with clear motivation to grow into ML and MLOps
Desirable skills (strong differentiators):
- Using AWS SageMaker for training, deploying, and operating machine‑learning workloads, or demonstrating equivalent experience on similar cloud ML platforms
- Exposing machine‑learning models via APIs (e.g. FastAPI‑based inference services) and operating them reliably at scale
- Applying MLOps practices, including model and version management, monitoring, and handling model or data drift
- Implementing advanced service patterns such as asynchronous processing, event‑driven architectures, or multi‑version services
- Serving LLM or GenAI‑based capabilities in production, including model serving, RAG pipelines, and inference controls
- Designing reusable, platform‑level services and shared ML patterns rather than one‑off implementations
- Managing cloud operational trade‑offs, including cost efficiency, latency, scalability, and reliability
Health and Safety Responsibilities:
- Fostering the safety culture leading by example.
- Following established safety procedures and reporting potential hazards promptly helps maintain a secure and efficient workplace.
- Participating in safety training sessions and adhering to preventive guidelines and procedures, the objective is minimizing risks and protecting yourself and the rest of your colleagues.
At Sage, we offer you an environment where you can grow professionally without compromising your personal well-being. Our benefits package is designed to provide stability, flexibility, and balance:
- Flexible benefits: exchange part of your salary and make tax savings on health insurance, meal and transport vouchers, childcare, and training.
- Well-being: Free access to the Calm app (for up to 5 users), 24/7 counseling, and emotional support from our Healthy Mind Coaches. We also offer self-care and parenting resources through the Cleo app.
- Flexible working: flexibility of working one hour in, one hour out, shortened workdays on Fridays and during the summer, and the opportunity to work from over 40 countries for up to 10 weeks per year through our Work Away program.
- Annual leave: 23 working days of vacation, 5 paid days per year for volunteering, and 5 additional paid days annually for personal or professional development.
- Extended leave: 7 extra days of maternity leave and 5 extra days of paternity leave, on top of the legal allowance, available after one year of service.
- Financial support: Life and disability insurance, salary advances of up to 3.5 times your net monthly pay, a €300 net marriage bonus, and access to Sage's employee stock purchase plan at a discounted rate.