ML Ops Engineer
1 week ago
You will provide technical contributions to the data science process.
In this role, you are the internally recognized expert in data, building infrastructure and data pipelines/retrieval mechanisms to support our data needsHow you will contributeYou will:
Operationalize and automate activities for efficiency and timely production of data visualsAssist in providing accessibility, retrievability, security and protection of data in an ethical mannerSearch for ways to get new data sources and assess their accuracyBuild and maintain the transports/data pipelines and retrieve applicable data sets for specific use casesUnderstand data and metadata to support consistency of information retrieval, combination, analysis, pattern recognition and interpretationValidate information from multiple sources.
Assess issues that might prevent the organization from making maximum use of its information assetsWhat you will bringA desire to drive your future and accelerate your career and the following experience and knowledge:Extensive experience in data engineering in a large, complex business with multiple systems such as SAP, internal and external data, etc.
and experience setting up, testing and maintaining new systemsExperience of a wide variety of languages and tools (e.g. script languages) to retrieve, merge and combine dataAbility to simplify complex problems and communicate to a broad audienceMore about this roleThis position provides a very significant amount of learning and growth opportunities from both technical and leadership perspective.The candidate will be exposed to different areas of Machine Learning operations and Maintenance and will Manage variety of modeling approaches.
MLOps engineers require a skill set that comes from multiple different fields like DevOps, Data Engineering and Machine Learning Operations.
Candidate will be preferred with the degree in the fields of; Computer Science, Engineering, Computational Statistics, MathematicWhat you need to know about this position:This role requires person to having working experience in the following areas: -Deployed ML services and applications to at least one major cloud platform (AWS, Azure, GCP) using Python and PySpark onto the Databricks environment.
Design, build and optimize application containerization and orchestration with Docker and Google Kubernetes Engine.Design, build and deployed microservices for Data Science application such as ML Training Pipeline, ML Serving Pipeline.
Knowledge on machine learning frameworks like Scikit-Learn, TensorFlow, PyTorch, Keras etc.
Proficiency with MLOps frameworks like MLFlow, Kubeflow, ML Tracking and Experiments.
Proficiency with Continuous Implementation and Continuous Delivery to deploy code into production.
Quantitative and Qualitative Model Validation process developed by Data Science and Analytics team and should have implemented the Model validation process pipeline in compliance with company policyBuilt continuous integration and delivery pipelines for Machine Learning applications.
Worked in agile pods to design and build cloud hosted, ML products with automated pipelines that run, monitor, and retrain ML Models.
Built products that will be deployed globally on a leading-edge tech stackExperience in setting up the ML Model Reports on usage of models, model performance monitoring.
What extra ingredients you will bring:
Enabled the basic data validation like format and size, column types, null and invalid values and also using higher level statistical property of input data like standard deviation etc.
Enabled the Data and Model Drift Alert FrameworkSetting up the version control on Code, Data, and Machine Learning ModelsRecommending the ideas to improve the Model performanceProvide effective challenge to the model design, development and conduct incremental analysis and testing as necessaryReview of back-testing exercises produced by the Modeling teamRecommendation aiming at ensuring the adequacy and the compliance of models (issue and monitoring)A core member of the Model Governance Team related to new model development and validationValidation of methodological choices and contribution as an expert to the model reviewA working knowledge of exact, approximation algorithms, and heuristic methods for solving difficult optimization problemsImplementation and maintenance of model validation processes and conduct validation and ongoing monitoring activities for new and existing models.
Provide assessment by writing a comprehensive validation report based on his/her judgment of the Model results.Job specific requirements:
8+ year of experience as DevOps, Data Engineer and MLOps engineer working on cloud-based services on Unix/Linux environment.
Experience is 2-3 ML Implementation project in the enterprise.
Experience leading discussions and developing project plans with cross-functional teams.
Proficiency in Python and PySpark language.
Experience in working with source code management systems like GitHub.
Within Country Relocation support available and for candidates voluntarily moving internationally some minimal support is offered through our Volunteer International Transfer PolicyBusiness Unit Summary
At Mondelēz International, our purpose is to empower people to snack right by offering the right snack, for the right moment, made the right way.
That means delivering a broad range of delicious, high-quality snacks that nourish life's moments, made with sustainable ingredients and packaging that consumers can feel good about.
We have a rich portfolio of strong brands globally and locally including many household names such as Oreo, belVita and LU biscuits; Cadbury Dairy Milk, Milka and Toblerone chocolate; Sour Patch Kids candy and Trident gum.
We are proud to hold the top position globally in biscuits, chocolate and candy and the second top position in gum.
Our 80,000 makers and bakers are located in more than 80 countries and we sell our products in over 150 countries around the world.
Mondelēz International is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation or preference, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
RegularData ScienceAnalytics & Data Science
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