The Senior ML Engineer will design, build, and optimize ML models, focusing on recommender systems and MLOps, primarily using AWS.
We are looking for a Senior ML Engineer to design, build, and optimize machine learning models and pipelines powering production systems. The ideal candidate brings deep hands-on experience across the ML lifecycle, with particular strength in recommender systems, deep learning, MLOps practices, and cloud-based ML infrastructure on AWS.
Requirements
- 4+ years of hands-on experience in machine learning engineering
- Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost, etc.).
- Solid experience with deep learning — architecture design, training, hyperparameter tuning, and deployment of neural network models.
- Proven experience designing and deploying recommender systems.
- Hands-on experience with AWS SageMaker and broader AWS ML ecosystem.
- Practical experience setting up data processing and ML workflows on AWS.
- Strong MLOps skills.
- Solid understanding of the full ML lifecycle.
- Hands-on experience with containerization and orchestration in production environments.
- Proficiency with SQL and experience working with both structured and unstructured data sources.
- Strong problem-solving skills with an emphasis on scalability and performance optimization.
- Design, train, and iterate on ML and deep learning models for recommendation, ranking, and personalization use cases.
- Architect and maintain end-to-end ML pipelines on AWS.
- Set up and optimize data processing and ML workflows using AWS services.
- Build and maintain MLOps infrastructure.
- Collaborate with data engineers to ensure data quality, build feature stores, and prepare datasets for model training and inference.
- Evaluate and benchmark model performance, run offline and online experiments, and drive continuous improvement of model accuracy and efficiency.
- Optimize model serving infrastructure for latency, throughput, and cost-effectiveness.
- Partner with product and business stakeholders to translate requirements into well-scoped ML solutions.
- Document model architecture, assumptions, performance characteristics, and known limitations.
- Stay current with advances in recommendation systems, deep learning, and cloud ML services, and propose improvements to existing approaches.
Similar Jobs
Artificial Intelligence • Information Technology • Consulting
The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.
Top Skills:
Amazon SagemakerAws BedrockAws LambdaDaskDockerEcrEmrLlmsMl AlgorithmsNlpPythonRecommendation EnginesS3Spark
Artificial Intelligence • Information Technology • Consulting
The role involves creating and improving ML models, collaborating with teams, developing experimentation roadmaps, and ensuring optimal performance of production models.
Top Skills:
AWSDockerPython
HR Tech • Software
Design and develop agentic workflows for intelligent recruiting systems, focusing on ML, NLP, collaboration, and integration with product teams.
Top Skills:
AIHugging FaceMachine LearningNatural Language ProcessingPythonPyTorchScikit-LearnTensorFlow
What you need to know about the London Tech Scene
London isn't just a hub for established businesses; it's also a nursery for innovation. Boasting one of the most recognized fintech ecosystems in Europe, attracting billions in investments each year, London's success has made it a go-to destination for startups looking to make their mark. Top U.K. companies like Hoptin, Moneybox and Marshmallow have already made the city their base — yet fintech is just the beginning. From healthtech to renewable energy to cybersecurity and beyond, the city's startups are breaking new ground across a range of industries.
