Job Description

About the Role:

We are seeking a highly skilled

Senior DevOps / MLOps Engineer

to join our team and drive the deployment and integration of machine learning projects across the enterprise. In this role, you will be responsible for building, maintaining, and optimizing the infrastructure and pipelines that support end-to-end machine learning workflows--from development to production.


You'll collaborate closely with data scientists, ML engineers, and business stakeholders to turn ideas into scalable, reliable, and secure solutions. This role is ideal for someone who thrives in fast-paced environments, enjoys problem-solving, and brings both technical depth and practical business sense to their work.

Key Responsibilities:

Design, implement, and maintain robust CI/CD pipelines for ML and software projects Support the full SDLC (Software Development Life Cycle), ensuring smooth integration, testing, deployment, and monitoring Build and manage ML model deployment pipelines, including containerization, versioning, rollback, and orchestration Automate testing, quality assurance, and performance checks for Python-based machine learning code Develop and maintain infrastructure-as-code solutions for repeatable and consistent environments Implement observability best practices, including monitoring, alerting, logging, and metrics Handle secrets management and enforce security practices in all DevOps processes Collaborate with cross-functional teams to translate business requirements into operational systems Identify and troubleshoot infrastructure and deployment issues, providing scalable solutions Document architectures, processes, and configurations clearly and concisely

Qualifications:

Must-Have Technical Skills:

Strong experience with general DevOps tooling and practices Proficient in

Python

, with experience in testing frameworks (e.g., pytest) Deep knowledge of

CI/CD tools

(e.g., GitHub Actions, Jenkins, GitLab CI, etc.) Familiarity with

SDLC

processes, change control, and release management Hands-on experience with ML pipeline orchestration tools (e.g., MLflow, Airflow, Kubeflow) Experience with

Lightspeed

for scalable ML workflows Proficient with

Helm

for Kubernetes application packaging and deployment Hands-on experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK) Solid understanding of

secrets management

(e.g., HashiCorp Vault, AWS Secrets Manager, CyberArk) Strong

SQL

skills for data validation and diagnostics Proficient with

Git

,

shell scripting

, and Linux environments Familiarity with containerization and orchestration (e.g., Docker, Kubernetes)

Soft Skills & Business Acumen:

Strong problem-solving and debugging skills High adaptability and comfort working in ambiguity Ability to translate loosely defined business needs into technical solutions Excellent communication skills for both technical and non-technical stakeholders A collaborative mindset and proactive attitude toward improvement

Nice to Have:

Experience deploying ML models in production at scale Familiarity with cloud platforms (AWS, GCP, or Azure) Exposure to data governance, access control, and compliance in ML workflows Understanding of feature stores and model registries

Why Join Us?

You'll be joining a forward-thinking, high-impact team building modern, scalable systems to power machine learning across the business. We value ownership, curiosity, and clarity. If you're excited about shaping the infrastructure behind production-grade ML, we'd love to hear from you.


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Job Family Group:


Technology
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Job Family:

Applications Development
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Time Type:


Full time
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Most Relevant Skills


Please see the requirements listed above.
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Other Relevant Skills


CI/CD, DevOps, Machine Learning Operations.
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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.


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