Azure AI DevOps Engineer

Role: Azure AI DevOps Engineer
Location: Weehawken, NJ
Experience: 8+ Years

Summary:
This resource will be part of our growing Infrastructure Automation Team within Public Cloud. This role will be key in working with various infrastructure teams (Security, Networking, Database, Unix) to develop, deploy and support cloud and hybrid applications. Due to the collaborative nature of the role, it will be important that this resource be senior in both AI/ML / AWS / technical skills and interpersonal skills. Candidate should have strong communication, teamwork, customer service and leadership skills to partner with broader global technology engineering, information security and line of business teams.

Responsibilities:

• Translate the vision of business leaders into realistic technical implementations.
• Select appropriate AWS and/or Azure technologies, considering deployment models and integration with existing tools.
• Collaborate with Enterprise Architecture, Application Development, and Business teams to engineer pilot use cases and develop production solutions.
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance.
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
• Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.

Preferred Qualifications:

• Bachelor's Degree in any of Computer Science, Statistics, Data Science, or a related field.
• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• 5 + years of experience in Data Science, Machine Learning, or AI environment
• Experience in implementing cloud-based AI/ML workloads on AWS and/or Azure.
• Experience gathering non-functional requirements, performing designed and validated application architecture frameworks, and executing functional and testing assignments.
• Experience in Generative AI/LLMs, preferably experienced in delivering and productionizing.
• Experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment.

Good to Have:

• Experience in Finance / Banking industry some understanding of Securities and Banking products.