MLOps Engineer

Job not on LinkedIn

🕒 March 31

🇺🇸 United States – Remote

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 Machine Learning Engineer

👻 Ghost score 53%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Moody's

WebsiteLinkedInAll Job Openings

10,000+ employees

💼 Consulting

🏥 Healthcare

🛡️ Insurance

Consulting • Healthcare • Insurance

Moody's is a global provider of data and insights, specializing in credit ratings, research, and risk management. The company serves a diverse range of clients including banking institutions, corporations, the buy-side, insurance companies, and public sector organizations. Moody's offers solutions for risk management, financial analysis, and compliance, leveraging technology such as artificial intelligence to optimize decision-making and help clients navigate uncertainty in financial markets.

📋 Description

• Work closely with the Data Science team and the Data Engineers and DevOps teams in order to deploy machine learning models • Execute continuous integration and continuous delivery (CI/CD) activities to release ML code and ML pipelines into a Production environment • Maintain the Machine Learning pipeline and make sure everything is running accurately and reliably • Liaise with senior stakeholders across the Data function and the wider business • Use industry best practices such as code reviews, pull requests, and peer testing to ensure high quality AI/ML deliverables • Build AI/ML model performance benchmarking, evaluation, monitoring capabilities and facilitates resolution of issues with the appropriate teams

🎯 Requirements

• Proven industry/commercial/research lab experience (2+ years) deploying machine learning models and maintaining ML pipelines, orchestration, deployment, monitoring, & support • Experience creating and maintaining deployment pipelines with CI/CD tools (2+ years) • Knowledge of cloud technologies (e.g. AWS) and Extensive Programming experience in Python & SQL • Experience in containerization and orchestration (such as Docker, Kubernetes) • Practical Knowledge of Machine Learning models in commercial settings

🏖️ Benefits

• Flexible work environment • Allow remote work depending on one’s personal choice