Senior Associate - DevOps​/MLOps Platform Engineer

Location Designation:
Hybrid - 3 days per week

When you join New York Life, you’re joining a company that values career development, collaboration, innovation, and inclusiveness. We want employees to feel proud about being part of a company that is committed to doing the right thing. You’ll have the opportunity to grow your career while developing personally and professionally through various resources and programs. New York Life is a relationship-based company and appreciates how both virtual and in-person interactions support our culture.

As part of Technology, you'll have the opportunity to contribute to groundbreaking initiatives that shape New York Life's digital landscape. Leverage cutting-edge technologies like Generative AI to increase productivity, streamline processes, and create seamless experiences for clients, agents, and employees. Your expertise fuels innovation, agility, and growth — driving the company's success.

New York Life’s AI and Data team delivers innovative data, insights, and AI solutions for the organization. Our AI team works on a diverse portfolio of AI and GenAI projects, by combining agile and entrepreneurial drive with industry-leading methods and tools. Our efforts are fully supported by executive leadership, and we work hand in hand with our Business Partners through all stages of model development from ideation to deployment.

As it takes multiple skill sets to deliver AI models to production, our AI team includes product managers, data scientists, MLOps engineers, program managers, a model validation & governance group, and a communications & development group.

You will contribute to the building and maintenance of a platform used to enable the development and deployment of AI & Data solutions. You will help to build and maintain an industry leading MLOps tech stack.

• Contribute to the design, build, and support of a modern MLOps platform used across AI & Data teams.
• Design, implement, and operate secure, scalable Dev Ops and MLOps CI/CD workflows for data/ML applications and services.
• Develop and maintain on prem and AWS based data/ML infrastructure, including containerized training/inference and workflow orchestration (e.g., Airflow).
• Partner with Technology (IT), SRE, and Security to integrate networking, identity, secrets management, and observability; ensure compliance and governance.
• Serve as a platform subject matter expert—contribute to solution design, codify best practices, and produce clear documentation/runbooks.
• Work independently and collaboratively as a part of a team.
• Support Scrum ceremonies by contributing to story creation and sprint goals.
• Effectively articulate information and ideas to a diverse group of people.
• Stay up to date with the latest MLOps trends/emerging technologies and look for opportunities to improve the stack.

Required Qualifications:

• 6+ years of industry experience in software engineering, data engineering, Dev Ops, MLOps or a related field.
• Expert-level experience with cloud compute environments (AWS) along with cloud-native tools; proficiency in Terraform a must.
• Experience with containers (Docker), container orchestration (Kubernetes) and process orchestration/DAGs (e.g., Airflow).
• Strong experience with AWS (e.g., EKS, EC2, S3, IAM, Cloud Watch) and cloud native tooling; familiarity with Sage Maker or equivalent a plus.
• Solid understanding of CI/CD and Dev Sec Ops  best practices; experience with Git branching strategies, PR/code review, and artifact/security scanning (e.g., Jenkins, Git Hub Actions, or Git Lab CI).
• Good command of SQL and with familiarity with an RDBMS
• Experience supporting both batch and real time inference (REST/gRPC, streaming).
• Proficiency in Python and Bash; working knowledge of Terraform and Helm for IaC.
• Have an agile mindset.
• Strong written and verbal communication; ability to work independently with minimal supervision while collaborating effectively in a team.
• Bachelor’s degree in computer science, Engineering, or a related field—or equivalent practical experience.

Preferred:

• Understanding of ML frameworks (sklearn, Tensor Flow, PyTorch, etc)
• Experience with big data/lakehouse…