AI/ML Infrastructure (Ops) Engineer

Published date Posted on Indeed on Aug 05, 2022 (14 d ago)

Come join a team of industry and science leaders to achieve a vision of empowering innovation through state-of-the-art artificial intelligence leveraging multiple cloud technologies. We are addressing exciting challenges for our customers, at the intersection of AI/ML and cutting-edge cloud infrastructure.


What You Would Do:

As an Applied AI/ML Infrastructure (Ops) Engineer, you will be working in conjunction with like-minded departments such as the Data Science team to build AI-powered solutions for our customers. You will be expected to build, evaluate and incubate new technologies to enhance our technical capabilities.

  • Demonstrate high competency in understanding the requirements of an AI-powered solution and structuring the development and releases of the software system.

  • Develop and adopt low-latency and scalable infrastructure back-end that leverage AI models.

  • Design, architect and implement the infrastructure solution in order to scale keeping in mind the performance and infrastructure costs associated with an AI system.

  • Collaborate with the data science, engineering, and company leadership, helping to set the strategy and standards for data science, engineering, and advanced analytics.

  • Endorse the latest data science and engineering practices within our organization to ensure scalability of our systems and mobility of development.

  • 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.


Your Qualifications are:

  • Entrepreneurial: dealing with ambiguity and working in a highly collaborative tech-startup environment while maintaining a customer-centric approach.

  • 2+ years working experience in an Infrastructure role with a strong Kubernetes experience.

  • 2+ years of Experience in Building & Deploying ML Models in Production for Healthcare, Finance, Insurance, Retail, Telecom, or Manufacturing.

  • BSc in Computer/Software Engineering, Science, or similar technical field.

  • Experience in MLOps, AutoML, and Big-Data Platforms such as Kubeflow, MLflow, Hadoop, Spark, H2O, Kubernetes, Docker.

  • Experience in developing AI models via classical ML and Deep Learning methods.

  • Experience in designing, building, and deploying highly scalable distributed ML models and/or software systems.

  • Experience in designing, building, and deploying end-to-end ML pipelines using DL frameworks like PyTorch and TensorFlow 2.0.

  • Extensive knowledge of Python and NodeJS, Experience with at least one MVC framework (e.g. Django, VueJS) and OOP language (e.g. GoLang)

  • Highly 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) including serverless environments such as AWS lambdas, Google cloud functions.

  • 3+ years of experience in software development cutting across data science, machine learning engineering, data 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 & Active Learning.

  • Experience in Hyperparameter Optimization Methods & Frameworks.

  • Exp. in Neural Architecture Search, and Model Compression / Distillation.

  • Experience with cloud infrastructure orchestration using scripting tools.


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