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To advance the organization by developing algorithms to build artificial inteligence and machine learning models that uncover connections and make better decisions without human intervention.
Role
Experimentation is at the core of what you do. The role is to work to turn business questions into data analysis effectively and provide meaningful recommendations. This is a unique hybrid role that will focus on your knowledge of data infrastructure and your ability to drive insights.
Authority
Develop models and train them,
Research on new technologies,
Participate in recruitment process.
Responsibility
Develop highly scalable systems, algorithms, and tools on one platform to support machine learning and deep learning solutions,
Develop, integrate, and optimize end to end AI pipeline,
Collect, analyze, and synthesize requirements and bottleneck in the technology, systems, and tools used by machine learning engineers and scientists, develop solutions that improve efficiency, leverage more amount of data efficiently,
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, GPU, TPU and FPGA),
Explore state-of-the-art deep learning techniques,
Partner with data science and domain engineering teams to support the business transformation through AI.
Requirements
University or advanced degree in engineering, computer science, mathematics, or a related field,
5+ 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, or 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, or 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.
Benefits
It's always a good idea to include the benefits of the job the company will provide such as:
Flexible hours to give you freedom and increase productivity
