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Lead Cloud Operations Engineer
2 months ago
Position Overview:
We are seeking a talented individual to transform the strategic vision of our leadership into actionable technical solutions.
As a Lead Cloud Operations Engineer, you will be responsible for selecting the most suitable AWS and/or Azure technologies, taking into account various deployment models and ensuring seamless integration with current tools.
In this role, you will collaborate with teams across Enterprise Architecture, Application Development, and Business sectors to design pilot projects and develop robust production solutions.
Your ability to gather insights from diverse stakeholders will be crucial in aligning technical implementations with both present and future needs.
Additionally, you will work closely with security and risk management teams to identify and mitigate potential risks, ensuring ethical AI practices and compliance are upheld.
Key Responsibilities include addressing issues such as training data integrity, safeguarding AI models, and managing adversarial inputs.
You will also be tasked with creating tools and capabilities that facilitate data ingestion, feature engineering, and effective data management.
Furthermore, you will design, implement, and sustain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of our machine learning infrastructure.
As part of your duties, you will contribute to MLOps and LLMOps, emphasizing operational capabilities and infrastructure necessary for deploying and managing machine learning models and large language models.
Preferred Qualifications:
A Bachelor's Degree in Computer Science, Statistics, Data Science, or a related discipline.
Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect.
A minimum of 5 years of experience in Data Science, Machine Learning, or AI environments.
Proven experience in implementing cloud-based AI/ML workloads on AWS and/or Azure.
Experience in gathering non-functional requirements, designing validated application architecture frameworks, and executing functional and testing assignments.
Familiarity with Generative AI/LLMs, particularly in delivering and operationalizing solutions.
Experience in machine learning model development, natural language processing, and data analysis; skilled in both Supervised and Unsupervised learning, feature engineering, model training, and deployment.