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Sr AI/ML Engineer
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Citizens Bank, N.A.
Providence, RI (In Person)
Full-Time
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Job Description
Description 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