Looking for a senior ML AI engineer that is well-seasoned in implementing and developing complex data science solutions.
Looking for a senior ML AI engineer that is well-seasoned in implementing and developing complex data science solutions. You will have experience designing, building or implementing ML business solutions, commercial experience with statistical models, machine learning, and natural language processing, experience with the likes of Keras, StackStorm, Python, TensorFlow, other AI/ML Frameworks. Will Understanding of Data Structures, Algorithms and Object-Oriented Design concepts.
A graduate with a Bachelor’s Degree in Computer Science, Data Science or equivalent training and experience. Concentrations in AI/ML preferred.5 – 7 years of experience with designing, developing, integrating, software engineering processes and software development life cycles. Proficient in Python, R, Java, and other important ML and analytics languages (e.g. R, Scala).
Passionate about the application of Data Science to solve problems Background in Big Data, Distributed Computing, AnalyticsExperience with design patterns and implementation and deployment AI and/or data science products.
Experience using Agile/Scrum methodologiesResourceful, able to drive towards results in a start-up style environment Is enthusiastic about sharing knowledge and collaborating with other teamsAn enthusiastic self-starter and team player
An excellent analytical and problem-solver Desired Skills Knowledge of data engineering and experience with big dataLinux and shell scripting expertiseProficiency with SQL and NoSQL databasesProficiency with scalable data extraction tools (e.g. Cassandra, MongoDB)
Proficiency with Scala, Spark, Java and/or SASExperienced in using AI/ML platforms, technologies, techniques (e.g. TensorFlow, Apache MXnet, Theano, Keras, CNTK, scikit-learn, H2O, Spark MLlib, etc).Experience developing, testing and deploying APIsExperience building applications based on Microservices Architecture
Familiar with techniques such as continuous delivery and continuous integration Experienced in deploying and managing infrastructures based on Docker, Kubernetes, or OpenStack, and public Clouds such as Azure, AWS or Google Cloud Platform.