The Sr ML Ops Engineer will have experience in deploying, monitoring, and managing machine learning models in production environments. You will be responsible for designing and implementing scalable and reliable ML pipelines.
Job Responsibilities
Design, implement, and maintain ML pipelines and infrastructures.
Collaborate with data scientists to deploy and monitor machine learning models.
Develop CI/CD pipelines for continuous integration and delivery of ML models.
Automate and streamline ML workflows and processes.
Troubleshoot and resolve issues related to ML model deployment and performance.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- 8+ years of experience in software engineering, DevOps, ML engineering, or MLOps-related roles.
- Proven experience deploying, monitoring, and managing machine learning models in production environments.
- Strong understanding of the ML lifecycle, including training, validation, deployment, monitoring, and retraining.
- Proficiency in Python and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience building and exposing scalable ML/LLM services using FastAPI or similar API frameworks.
- Strong experience designing and implementing CI/CD pipelines for ML and software delivery.
- Experience with MLOps and workflow orchestration tools such as MLflow, Kubeflow, Airflow, SageMaker, or similar platforms.
- Experience designing and orchestrating Retrieval-Augmented Generation (RAG) pipelines, including embeddings, vector databases, and re-ranking techniques.
- Familiarity with building agentic workflows, including tool integration, multi-step orchestration, and reasoning pipelines.
- Hands-on experience enabling and integrating Large Language Models (LLMs) for enterprise use cases, including prompt engineering and inference optimization.
- Understanding of LLM deployment patterns across platforms such as AWS SageMaker, Bedrock, Azure OpenAI, or self-hosted environments.
- Experience implementing guardrails, monitoring, and evaluation frameworks for LLM outputs (quality, safety, hallucination detection).
- Strong experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud, including managed ML services.
- Knowledge of monitoring, logging, and observability tools for tracking system and model performance.
- Strong understanding of version control, testing frameworks, and software engineering best practices.
- Ability to troubleshoot complex deployment, scaling, and performance issues in distributed systems.
- Experience with model governance, security, compliance, and reproducibility practices is a plus.
- Strong collaboration and communication skills, with the ability to work effectively across data science, engineering, and platform teams.
Hours & Work Schedule
- Hours per Week:40
- Work Schedule:Monday-Friday
- Hybrid: 4 days per week on-site
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
Equal Employment and Opportunity Employer
Job Applicant Data Privacy Policy
Background Check
Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.
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