Machine Learning Specialist
- Software and Hardware
- 5 to 9 Yrs
- karnataka
- 6 days ago
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Job Description
- You will be responsible for deploying and maintaining machine learning models, pipelines, and workflows in a production environment. Your role will involve re-packaging ML models developed in the non-production environment for deployment to the production environment, as well as refactoring non-production ML model implementations to an "ML as Code" implementation. You will create, manage, and execute ServiceNow change requests to facilitate the deployment of new models, and build scalable, reliable, and efficient machine learning infrastructure. Collaboration with data scientists and software engineers to design and implement machine learning workflows will be a key aspect of your role. Monitoring and logging tools will be implemented by you to ensure optimal performance of machine learning models, and you will be responsible for identifying and evaluating new technologies to enhance performance, maintainability, and reliability of machine learning systems. Your responsibilities will also include applying software engineering best practices to machine learning, supporting model development with a focus on auditability, versioning, and data security, as well as creating and maintaining technical documentation for machine learning infrastructure and workflows. Staying updated with the latest advancements in machine learning and cloud computing technologies will be crucial. You will provide expertise in data PaaS on Azure storage, big data platform services, server-less architectures, Azure SQL DB, NoSQL databases, and secure automated data pipelines. Working collaboratively and exercising sound judgement in developing robust solutions while seeking guidance on complex issues will be essential. Basic qualifications for this role include a Bachelor's or Master's degree in computer science, engineering, or a related field, along with at least 5 years of experience in software development, machine learning engineering, or a related field. A strong understanding of machine learning concepts and frameworks, hands-on experience in Python, and familiarity with DevOps practices and tools like Kubernetes, Docker, Jenkins, and Git are must-have skills. Experience in developing and deploying machine learning models in a production environment, working with cloud computing and database systems, building custom integrations between cloud-based systems using APIs, and maintaining ML systems with open-source tools are also required. Additionally, experience in developing with containers and Kubernetes in cloud computing environments, ability to translate business needs into technical requirements, and proficiency in data pipeline design, development, and delivery are necessary. Strong analytical and problem-solving skills are essential for this role. Good to have skills include knowledge of cloud migration methodologies and processes, experience with Hadoop file formats and compression techniques, DevOps on an Azure platform, familiarity with developer tools like Visual Studio, GitLabs, Jenkins, and experience with private and public cloud architectures. Proven ability to work independently and in a team-oriented environment, as well as excellent written and oral communication skills, organizational skills, and multitasking abilities, will be beneficial for this role. Experience with MLOps in Azure and Azure native data/big-data tools, technologies, and services is preferred.,
Employement Category:
Employement Type: Full timeIndustry: IT Services & ConsultingRole Category: Not SpecifiedFunctional Area: Not SpecifiedRole/Responsibilies: Machine Learning SpecialistContact Details:
Company: Promaynov ExecutiveLocation(s): Karnataka+ View Contact
Keyskills: Python DevOps Kubernetes Docker Jenkins Git Cloud Computing Database Systems Hadoop Visual Studio Azure Data Factory
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Software and Hardware
An American software, hardware, and services technology company. It also does hardware development of global navigation satellite system (GNSS) receivers, scanners, total stations, laser rangefinders, unmanned aerial vehicles (UAVs), inertial navigation systems and software processing tools.
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