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Mid-level MLops role with broad cloud/DevOps requirements at a known financial firm increases candidate competition.
ML/Ops skills transfer across industries but preferred retail financial services experience raises domain specificity.
Explicit 2–3 year requirement plus specific AWS, EKS, MLflow, and CI/CD skills increases shortlisting strictness.
Design and implement scalable, resilient, and secure productionized AI/ML/DL/LLM models integrated into business processes.
Monitor and optimize deployed AI model performance and support platform health.
Develop and manage CI/CD release pipelines for AI models, coordinating deployment with DevOps and release management teams.
Bachelor’s Degree in Computer Science, Engineering, or related field.
2-3 years experience in AI Operations, Machine Learning Engineering, Data Science, or related fields, preferably in retail financial services or IT with agile methodologies.
Experience with DevOps, AWS (including EKS), Docker, API development, MLflow, Apache Airflow, Azure DevOps, and GitHub technologies.
Ability to travel up to 5% annually.
Experienced in AI Operations with a focus on end-to-end deployment and productionization of ML/DL/LLM models.
Skilled in creating and managing CI/CD pipelines and collaborating cross-functionally with data science, DevOps, and infrastructure teams.
Comfortable working in agile environments within retail financial service or IT sectors, managing deployment risks and model lifecycle at scale.