Come join a team of industry and science leaders to achieve a vision of empowering innovation through state-of-the-art artificial intelligence and machine learning. We are addressing exciting challenges for our customers, at the intersection of AI/ML and cutting-edge cloud infrastructure with ML being both a core enabler for and a major feature of, our platform.
We are looking for candidates who are adept in AI/ML engineering and infrastructure engineering capabilities…
- Design, architect, and implement the infrastructure solution in order to scale, keeping in mind the performance and infrastructure costs associated with an AI system.
- Develop and adopt low-latency and scalable infrastructure that leverage AI models.
- Demonstrate high competency in understanding the requirements of an AI-powered solution and structuring the development and releases of the software system.
- Endorse the latest data science and engineering practices within our organization to ensure the scalability of our systems and mobility of development.
- Collaborate with the data science, engineering, and company leadership, helping to set the strategy and standards for data science, engineering, and advanced analytics.
- Conceive and prototype innovative AI products and solutions to enable our current and potential customers to adopt aiXplain’s platform.
- Innovate, design, develop, test, deploy, maintain, and enhance software solutions, along with managing project priorities, deadlines, and deliverables.
- Entrepreneurial: dealing with ambiguity and working in a highly collaborative tech-startup environment while maintaining a customer-centric approach.
- 2+ years of working experience in an infrastructure role with strong Kubernetes experience.
- BSc in Computer/Software Engineering, Science, or a similar technical field.
- Experience in designing, building, and deploying end-to-end ML pipelines using DL frameworks like PyTorch and TensorFlow 2.0.
- Experience in MLOps, AutoML, and big-data platforms such as Kubeflow, MLflow, Hadoop, Spark, H2O, Kubernetes, and Docker.
- Experience in designing, building, and deploying highly scalable distributed ML models and/or software systems.
- Knowledge of Python and experience with at least one MVC framework (e.g., Django) and one statically typed language (e.g., C++ or GoLang).
- Experienced with SQL/NoSQL databases and big-data platforms (MongoDB, Hadoop, Elasticsearch, Redis, Cassandra, Spark, H2O).
- Experience configuring, scripting, and managing cloud infrastructure environments (AWS, Azure, GCP, and Linux/Unix administration).
- 3+ years of experience in software development, machine learning engineering, infrastructure engineering, and backend engineering.
- Kaggle achievements and/or open-source project contributions.
- Delivered talks for machine learning software or infrastructure in tech conferences, preferably applications involving AI/ML.
- Excellence in deep learning frameworks: PyTorch, TensorFlow 2.0.
- Experience in unsupervised, semi-supervised, and active learning.
- Experience in hyperparameter optimization methods and frameworks.
- Experience in neural architecture search, and model compression/distillation.
- Experience with cloud infrastructure orchestration using scripting tools.