Life with Quantum is more than just project delivery. See for yourself.
Quantum is a data analytics and software engineering company with an international presence and R&D center. We are looking for progressive solutions to real problems, leaving the comfort zone. This allows us to remain the leaders of the Big Data market.
You will be a part of the company and surrounded by experts who are ready to move forward professionally.
About the project
Our client is a digital transformation, cognitive computing, artificial intelligence software company. Their product is a highly scalable and customizable platform that serves as an AI Operating system. This system is used for a wide range of capabilities such as business intelligence, decision making, forecasting, and situational awareness. The product also contains cognitive computing applications configured for specific industries and business functions.
Must have skills
- A strong predilection for good software and the processes that make it;
- 3+ years of experience in MLOps, working with ML engineers building tooling and automation for ML;
- 5+ years of overall engineering experience related to ML;
- 5+ years of Python development experience;
- 4+ years of experience with AWS or other public cloud platforms (GCP,
- Azure, etc.);
- Experience with at least one of cloud-native MLOps solutions, such as AWS SageMaker/SageMaker Pipelines, GCP Vertex AI, Azure Machine Learning;
- Experience with NLP, Machine Learning & Deep Learning Frameworks such as spaCy, scikit-learn, PyTorch, HuggingFace tools, etc.;
- Experience in setting up CI/CD/CT pipelines;
- Excellent verbal and written communication skills for effective communication in a multicultural environment with teams spread throughout the world.
Nice to have skills
- Experience with Kubernetes, KubeFlow & other DevOps and MLops tools;
- Experience developing data engineering solutions & pipelines;
- Familiarity with Knowledge Engineering, Graph technologies & Graph databases, Knowledge Graphs.
You will be responsible for
- Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning;
- Collaborate with teams to drive the ML infrastructure roadmap;
- Support company’s internal ML teams with MLOps best practices;
- Build complex automated reproducible pipelines for the entire MLOps lifecycle, including data management, model retraining, deployment into production and maintenance;
- Help establish standards, practices for managing the company’s ML infrastructure;
- Write clean and tested code that can be maintained and extended by fellow engineers;
- Collaborate on managing ML infrastructure costs.
- Exchange of experience, professional development;
- A strong team, a healthy atmosphere;
- Flexible working time;
- 20 days paid vacation;
- Paid sick leave;
- Remote work;
- 8-hour working day and 5-day working week;
- Opportunity to take part in conferences, meetups, etc. (fully or partially paid by the company);
- Regular company events.