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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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Strong background in platforms and tools such as OpenAI, Azure Semantic Kernel, AWS Bedrock, and other machine learning technologies, with a focus on Generative AI and open-source AI frameworks.
$218,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with cloud platforms such as AWS, Azure, or GCP. Familiarity with machine learning concepts and frameworks is a plus. Technology requirements: SQL GCP AWS Degree or certifications required: No degrees required Years experience: 10 years of experience as a Software Engineer or related role.
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As a Capital One Lead Software Engineer, you'll have the opportunity to be on the forefront of driving a major transformation within Capital One. 2+ years of experience with OpenSearch, Kafka, Vue.js and some exposure to Machine Learning.
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Experience designing and deploying cloud-native, enterprise-scale machine learning solutions in the AWS Cloud or with another major cloud provider. Amazon has been investing in Machine Learning for decades, and by joining AWS you’ll join a community of scientists and engineers developing leading edge solutions for enterprise-scale data science applications.
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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 designAbility to align AI/ML initiatives with broader business goals and outcomes, understanding how AI can drive value and competitive advantage.
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Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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Cloud certifications such as AWS Solutions Architect Associate/Professional, AWS SysOps Administrator, AWS Developer, or AWS DevOps Engineer. Create comprehensive documentation that outlines the architecture, design decisions, security measures, and processes implemented within the DevSecOps pipeline.
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As a Senior Machine Learning Engineer, you will work on building AI/ML solutions across a wide range of business applications within The Friedkin Group of companies.
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Work collaboratively with user experience designers, platform engineers, product owners, solution architects, and business analysts, the Cloud Applications Full Stack Developer will design, build, test, and deploy high-value, operationally resilient software products on the AWS cloud platform that satisfy functional and non-functional requirements.
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The Cloud Network Engineer is a technologist with demonstrated experience and interest in networking, public cloud, workload migration from private to public cloud, cloud native design and application transformation/modernization.
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Certifications such as Certified Information Systems Security Professional (CISSP), Certified Data Privacy Solutions Engineer (CDPSE), GIAC Machine Learning Engineer (GMLE) are highly desirable.
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Proven experience in data science, machine learning, or a related role, with a strong preference for experience in cybersecurity. Designing, developing, and implementing machine learning models to improve cybersecurity measures, including but not limited to intrusion detection systems, malware analysis, and fraud detection, user behavior anomaly detection.
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Experience with cloud security tools across platforms like AWS, Azure, or Google Cloud. Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers., Autoencoders, ML-Ops processes.
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We are seeking a talented and experienced Machine Learning Operations (ML Ops) Engineer with 2-4 years of hands-on experience in deploying and managing machine learning models in a Microsoft Azure environment.
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