Production Machine Learning Engineer deploying advanced perception models in Toyota's autonomous vehicle platform. Join a team innovating in mobility solutions while ensuring safety and reliability.
About the role
- Machine Learning Engineer responsible for designing AI applications leveraging Generative AI and LLMs at Citi. Involves architecting scalable platform services and collaborating with cross-functional teams.
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.
- 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.
- 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.
Benefits
- medical, dental & vision coverage
- 401(k)
- life, accident, and disability insurance
- wellness programs
- paid time off packages including planned time off (vacation), unplanned time off (sick leave), and paid holidays
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