Machine Learning Engineer - VP

Citi•Jersey City, NJ

49d

About The Position

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.

Requirements

5+ years of relevant experience in Software Development or a Systems Analysis role, with a strong focus on machine learning.
Strong object-oriented programming skills with expert-level proficiency in Python.
Deep experience with AI/ML frameworks (e.g., Langchain) and hands-on experience with Generative AI technologies and Large Language Models.
Proven experience building applications that apply LLMs and GenAI to use cases such as search, chat agents, and guided analytics.
Strong, working knowledge of DevOps tools (e.g., Jenkins, GitLab, Docker, Kubernetes) and Infrastructure as Code (IaC) practices.
Solid understanding of modern software development practices, including microservices architecture and API design principles.
Experience with various data storage solutions (e.g., NoSQL, SQL, data lakes) and data pipeline orchestration.
Excellent analytical and problem-solving skills with a proven ability to troubleshoot complex technical issues and provide innovative solutions.
Experience with various cloud platforms (e.g., AWS, GCP, Azure) and architectures (e.g., single/multi-tenant).
Experience designing and implementing microservices-based architectures for large-scale platforms in complex environments.
Demonstrated leadership, project management skills, and a proven track record of self-motivation and continuous learning.

Responsibilities

Design and architect end-to-end platform capabilities and services that integrate with Generative AI technologies.
Develop and deploy solutions using VectorRAG and GraphRAG techniques with both open-source and commercial Large Language Models.
Apply a strong understanding of Agentic AI workflows and frameworks to build sophisticated, automated systems.
Implement robust CI/CD pipelines, automated testing, and containerization (Docker, Kubernetes) to streamline the development and deployment of machine learning models.
Manage your deliverables from conception to deployment, proactively identifying and mitigating risks to ensure project goals are met.
Perform and lead quick proof-of-concept (POC) projects to evaluate and demonstrate the feasibility of new GenAI use cases.
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.
Work closely with cross-functional teams to solve complex problems, adapt quickly to changing priorities, and contribute to a culture of innovation.

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