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Proven experience with enterprise-scale technical delivery experience with AI Services including OpenAI, Schematic Kernel, AWS Bedrock, Machine Learning (or equivalent), Generative AI, LLM customization, NLP, Search, MLOps, Open-source AI frameworks, AI Infrastructure, architecture design.
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Cloud software, infrastructure, network security hardware / software, - You will focus on driving adoption and efficient use of the source-to-pay process through system optimization and proactive partner outreach along with sourcing management, contract management, and negotiation support where applicable.
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Investigate & solve problems relating to software enabled business function modernization, hosting services such as infrastructure, enterprise managed services, Robotic Process Automation (RPA), Machine Learning (ML), Artificial Intelligence (AI), data support to the tactical edge, and optimizations of commercial cloud hosted systems.
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Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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Expertise in IT security principles, particularly related to cloud infrastructure (Azure, AWS, Google Cloud), networks, databases, application security, firewalls, multi-factor authentication (MFA), and identity/access management.
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Platforms: Nvidia AI, Nvidia DGX, AWS, Google Cloud for deploying scalable solutions. Programming Languages: Python, R, or similar for AI and machine learning development. Serve as a trusted advisor to decision-makers, offering consultative insights on how the Nvidia DGX AI platform and machine learning can be applied to their biological data challenges.
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Experience developing and deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP). As a Senior Machine Learning Engineer, you will work on building AI/ML solutions across a wide range of business applications.
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Proficiency in Python, R, and other programming languages for developing machine learning models. Familiarity with cloud-based computing solutions (AWS, Google Cloud) for scalable data analysis.
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This includes enterprise and academic technology strategy, information security and training, user and workplace support, productivity and collaboration applications, application management, web content management, information management and business analytics, application development, voice services, data center and cloud storage, classroom technology, learning management systems, network services and technology installation and campus-wide user support services.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Experience in utilizing cloud-based data governance solutions, including Open Policy Agent (OPA), Gatekeeper, AWS Data Governance tools, Google Cloud Data Catalog/DataPlex, or Microsoft Azure Purview, to manage and govern data across various cloud environments.
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You have meaningful experience negotiating SaaS commercial transactions and strong knowledge/experience counseling on legal issues related to data privacy, information security, artificial intelligence, and intellectual property, including open source software.
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I'm supporting a brilliant company in Houston that are building a brand new Data and Analytics team to utilize cutting edge technology focused around Machine Learning and Artificial Intelligence.
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If you've used Disney+, Peacock, or other top streaming platforms, you've already benefited from Conviva's technology. For more information, visit us at. Examples of the brands Conviva has helped innovate, adapt, and scale at unprecedented speed include: DAZN, Disney+, HBO, Hulu, NBCUniversal, Paramount+, Peacock, Sky, Sling TV, Univision and Warner Bros Discovery.
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Lead on the management and maintenance of our cloud supporting infrastructure, AWS, and collaborate with the Machine Learning Engineer on its development; Strong knowledge using Vercel, Amplify, Google Cloud, Microsoft App Centre, Azure, Apple Connect, Google Console, Github, BugSnag, Slack, Jira for Software (Atlassian.
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