Machine Learning Ops Engineer

Job Description

We are looking for a hardworking, aspirational and innovative engineer for the ML Engineer position in our AI engineering and innovation team. The ML engineer will play a diverse and far-reaching role across organizations influencing adoption of technical solutions, strategies and design patterns across multiple teams and partners within Kimberley-Clark.This team is mainly responsible for the scalable deployment, interpretation and monitoring of these models. We are looking for a highly motivated and qualified MLOps engineer to help drive our MLOps and AI governance initiatives. This role is ideal for candidates with strong, hands-on skills in machine learning, model monitoring, and taking machine learning models to production including continuous integration, continuous delivery, and continuous training.Responsibilities:

• Apply ML expertise to train models, validates the accuracy of the models, and deploys the models at scale to production
• Work with the data science leadership, engineering leadership and product leadership to define processes for monitoring and governance of AI models on the MLOps platform
• Demonstrate exceptional impact in delivering projects, products and/or platforms in terms of scalable data processing and application architectures, technical deliverables and delivery throughout the project lifecycle
• Work on developing the Intelligence layer MLOps pipelines, ensuring that infrastructure and code is delivered incrementally and according to our internal security deployment policies
• Work directly with stakeholders, engineers, and data scientists to create high quality solutions that solve end-user problems
• Explore and recommend new tools and processes which can be leveraged across the AI lifecycle for capabilities and efficiencies
• Build MLOps pipelines to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models
• Evaluate open source and proprietary technologies and present recommendations to automate machine learning workflows model training, versioned experimentation, digital feedback and monitoring
• Develop and disseminate innovative techniques, processes and tools, that can be leveraged across the AI product development lifecycle.

• Degree in Computer Science, Statistics, Information Systems or another quantitative field with 5 years of ML engineering experience
• 5 to 7 years of demonstrated experience in developing highly scalable, reliable, and real-time data processing pipelines combined with experience in Machine Learning workflow and model deployments
• 4 years of demonstrated experience in developing ML pipelines on various frameworks on AWS, Azure, or GCP
• 2 years of experience in model monitoring, explainability, model management, version tracking, and AI governance
• Experience in cloud based solutioning and managing enterprise grade end to end machine learning solutions with automated pipelines for data processing, feature engineering, training, evaluation, deployment, integration and monitoring
• Hands-on experience with Docker, Kubernetes and machine learning tools like Azure Machine Learning, Amazon Sagemaker, MLFlow, KubeFlow, etc. in production
• Experience with one or more programming languages such as Java, Scala or Python
• Strong knowledge in one of machine learning design principles, ML Ops best practices or Big Data architectures
• Technical experience in AI, machine learning, predictive modelling, Natural Language Processing (NLP), Deep learning, advanced analytics
• Knowledge on SQL/NoSQL databases, Microservices and REST APIs
• Strong Knowledge of source code management, configuration management, CI/CD, security and performance
• Ability to look ahead to identify opportunities and thrive in a culture of innovation
• Self-starter who can see the big picture, and prioritize your work to make the largest impact on the business vision and requirements
• A can-do attitude in anticipating and resolving problems to help your team to achieve its goals
• Experience in Agile development methods.

Keyskills :
natural language processingdata sciencedesign patternsdata processingbig dataopen sourcedeep learningrealtime datastatements of work sow