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Machine Learning Engineer Vice President
- Tampa, Florida, United States
- Tampa, Florida, United States
Über
Architect and Build AI Solutions: Design and architect end-to-end platform capabilities and services that integrate with Generative AI technologies. Implement RAG Solutions: Develop and deploy solutions using VectorRAG and GraphRAG techniques with both open-source and commercial Large Language Models. Develop AI Workflows: Apply a strong understanding of Agentic AI workflows and frameworks to build sophisticated, automated systems. Drive DevOps Best Practices: Implement robust CI/CD pipelines, automated testing, and containerization (Docker, Kubernetes) to streamline the development and deployment of machine learning models. End-to-End Project Ownership: Manage your deliverables from conception to deployment, proactively identifying and mitigating risks to ensure project goals are met. Rapid Prototyping: Perform and lead quick proof-of-concept (POC) projects to evaluate and demonstrate the feasibility of new GenAI use cases. Ensure System Integrity: Appropriately assess and manage risk in all technical and business decisions, ensuring compliance with applicable laws, rules, and regulations, and maintaining transparency in reporting and managing control issues. Collaborate and Innovate: Work closely with cross-functional teams to solve complex problems, adapt quickly to changing priorities, and contribute to a culture of innovation.
Required Qualifications
Experience: 5+ years of relevant experience in Software Development or a Systems Analysis role, with a strong focus on machine learning. Programming Skills: Strong object-oriented programming skills with expert-level proficiency in Python. AI/ML Expertise: Deep experience with AI/ML frameworks (e.g., Langchain) and hands-on experience with Generative AI technologies and Large Language Models. Application Development: Proven experience building applications that apply LLMs and GenAI to use cases such as search, chat agents, and guided analytics. DevOps Knowledge: Strong, working knowledge of DevOps tools (e.g., Jenkins, GitLab, Docker, Kubernetes) and Infrastructure as Code (IaC) practices. Architectural Understanding: Solid understanding of modern software development practices, including microservices architecture and API design principles. Data Proficiency: Experience with various data storage solutions (e.g., NoSQL, SQL, data lakes) and data pipeline orchestration. Problem-Solving: Excellent analytical and problem-solving skills with a proven ability to troubleshoot complex technical issues and provide innovative solutions.
Education: Bachelor's Degree in Computer Science, Computer Engineering, Information Systems or equivalent work experience Desired Skills
Cloud Architecture: Experience with various cloud platforms (e.g., AWS, GCP, Azure) and architectures (e.g., single/multi-tenant). Large-Scale Systems: Experience designing and implementing microservices-based architectures for large-scale platforms in complex environments. Leadership: Demonstrated leadership, project management skills, and a proven track record of self-motivation and continuous learning.
Sprachkenntnisse
- English
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