This is a great opportunity to assume a technical position in a fast-growing and ambitious company. You will get a chance to address some of the most challenging real-world problems in the field of ML systems development and operations. You will work closely with Machine Learning engineering teams to anticipate company needs and deploy in-house developed ML solutions. You will have the opportunity to work on a wide range of MLOps tasks related to model deployment, operations and infrastructure, and to proactively help shape the future of the MLOps in the company. You will help create, improve and extend the underlying infrastructure that powers our ML teams, thus simplifying the development and deployment cycles of our ML solutions.
Responsibilities:
- Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning at BlackSwan Technologies
- 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
Requirements:
- 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 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:
- 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
