Artificial Intelligence/Machine Learning Engineer

Responsibilities:

• Architect, build, maintain, and improve new and existing suites of algorithms and their underlying systems,
• Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance testing, and A/B testing,
• Utilize your entrepreneurial spirit to identify new opportunities to optimize business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset,
• Work closely with data scientists and analysts to create and deploy new product features,
• Establish scalable, efficient, automated processes for data analyses, model development, validation, and implementation,
• Write efficient and well-organized software to ship products in an iterative, continual-release environment,
• Contribute to and promote good software engineering practices across the team,
• Knowledge sharing with the team to adopt best practices,
• Actively contribute to and reuse community best practices.

Requirements:

• University or advanced degree in engineering, computer science, mathematics, or a related field,
• 2+ years experience developing and deploying machine learning systems into production,
• Strong experience working with a variety of relational SQL and NoSQL databases,
• Strong experience working with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with at least one cloud provider solution (AWS, GCP, Azure),
• Strong experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc,
• Ability to work in a Linux environment,
• Industry experience building innovative end-to-end Machine Learning systems,
• Ability to quickly prototype ideas and solve complex problems by adapting creative approaches,
• Experience working with distributed systems, service-oriented architectures, and designing APIs,
• Strong knowledge of data pipeline and workflow management tools,
• Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation,
• Relevant working experience with Docker and Kubernetes is a big plus.