This is a technical & consulting role within our consulting team and part of the data engineering group. The goal is to design and develop solutions for various organizations looking to implement tools, software, and processes that support machine learning and AI initiatives. This role uses your abilities in cloud development, automation, DevOps, data engineering, automating and streamlining IT infrastructure processes and tasks to develop such solutions for organizations in healthcare, life sciences, biotech, electronics, public sector, manufacturing, agriculture, and retail sectors.
SFL Scientific is a data science consulting and professional services company, providing a broad range of solutions in data engineering, machine learning, and Artificial Intelligence. We provide strategy, prototype, integrate, and manage sophisticated AI solutions by leveraging emerging technology. With a globally connected network of technology partners, SFL Scientific brings world-class capabilities, delivering professional services and insights to address the most complex business challenges.
Our team solves complex and R&D type problems, tackling and helping organizations solve some of their most complex challenges with mathematics, data science, data engineering, and emerging technology. We are platform agnostic and are committed to providing the best technical solutions for each client and problem. Join us in Boston to build a technical career through consulting and professional services.
• Work with clients and their teams to design, develop, and deploy architectures for machine learning & automation applications such as ETL functions, compute infrastructure, parallelization, and optimization of DevOps procedures.
• Collaborate with colleagues to support and improve architecture, systems, processes, standards, and tools.
• Participate in architectural discussions to ensure solutions are designed for successful deployment, security, and high availability in the cloud
• Write and maintain code for automating the creation of scalable/resilient systems/infrastructure
• Educate/mentor data scientists and teams on best practices
• Bachelor’s degree in computer science or related focus, or equivalent experience.
• Knowledge of the various services and capabilities of computing platforms (AWS/Azure/GCP)
• Expertise with AWS such as IAM, EC2, EBS, ELB, RDS, S3, redshift, CloudWatch, Lambda scripts, Athena
• Expertise with Azure, and similar functionality services as above
• Strong knowledge and understanding of CI/CD processes and tools (Jenkins, Bamboo)
• Experience managing and supporting Docker, Kubernetes, Spark, Dask, Flask
• Shell, Python, Groovy, Powershell experience is a must
• Strong verbal & written communication skills and demonstrated ability of working with outside firms as a consultant
• Understanding of agile and other development processes and methodologies
• Understanding of immutable infrastructure and infrastructure as code concepts
• Linux (RHEL/CentOS) and Windows system administration experience required
• Experience with provisioning and configuration management tools, Ansible, HashiCorp Packer, Puppet, Chef, Terraform, CloudFormation, etc.
• AWS/Azure Certifications a plus (AWS/Azure Certified: SysOps Administrator, DevOps Engineer, Solutions Architect)
Location: Greater Boston Area
Client Travel Requirement: Minimal, less than 5% per year
Benefits: Full + 401k
Flexible/Remote Hours: Available/as required
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SFL Scientific is a US-based data science consulting and services company, providing a broad range of services and solutions in data engineering, big data, machine learning, and Artificial Intelligence. SFL works at the intersection of business and technology to help clients improve their performance and create sustainable value for stakeholders using custom algorithm development and data-driven solutions. We are industry agnostic and instead focus on solving complex, R&D, and challenging business problems. Learn more at sflscientific.com
Comprehensive benefits, healthcare, dental, vision, parental leave/support, 401k, flexible work and commuter specific work programs, and more.
Python, R, C/C++, Scala, Java, Tableau, R-Shiny, Hadoop, Spark, AWS, MongoDB, Cassandra, SQL/PostgreSQL, Docker, Kubernetes, Puppet, Chef, Ansible, Flask, Dask, and so on.
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