Machine Learning Engineer - VP at Citi

Jersey City, New Jersey, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

0.0

Posted On

20 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Generative AI, Large Language Models, Python, DevOps, AI Frameworks, Cloud Architecture, Microservices, Data Storage, Problem-Solving, CI/CD, Containerization, Architectural Understanding, Application Development, Rapid Prototyping, Leadership

Industry

Financial Services

Description

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. 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. 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. ------------------------------------------------------ AI Agents, AI Frameworks, AI Ops, Generative AI, Large Language Models (LLMs), Machine Learning (ML), ML Frameworks, Python (Programming Language), Standard ML (Programming Language). ------------------------------------------------------ Anticipated Posting Close Date: Dec 26, 2025 ------------------------------------------------------

Responsibilities

The role involves designing and architecting AI solutions, implementing RAG solutions, and managing projects from conception to deployment. Additionally, the engineer will collaborate with cross-functional teams to innovate and solve complex problems.