| Opportunity for Machine Learning Engineer and AI Engineer -onsite role at San Francisco, California, USA |
| Email: [email protected] |
| From: Nora West, W3Global [email protected] Reply to: [email protected] Hi , You have let your resume speak for yourself. Extremely inclined towards talking to you for a Machine Learning Engineer/AI Engineer -onsite position. Let me know the best time/number to reach or, you may reply or call me at (469) 287-8417 Ext: 5274. Job Title : Machine Learning Engineer/AI Engineer -onsite Employment Type : Contract, C2C Location : San Francisco , CA, US Job Description Job Title: Machine Learning Engineer/AI Engineer Tax Work Location: San Francisco, CA /Onsite Years of experience - 9+ MLOps / ML Engineering => 8/10 Platform Development / MicroServices / Arch => 7/10 Docker/Containers/Kubernetes => 6/10 Data Science / Machine Learning => 5/10 Azure - Highly preferred to have the experience Python - must have Spark- Required ML tools experience such as AzureML/MLFlow/Databricks/Kubeflow etc. - Deployed & worked on some of these tools. Summary of the project/initiatives which describes what's being done: o Build, modernize and maintain the U.S. Bank AI/ML Platform & related frameworks / solutions. o Participate and contribute in architecture & design reviews. o Build/Deploy AI/ML platform in Azure with open-source applications (Argo, Jupyter Hub/Kubeflow) and/or cloud/SaaS solutions (Azure ML, Databricks). o You will design, develop, test, deploy, and maintain distributed & GPU-enabled Machine Learning Pipelines using K8s/AKS based Argo Workflow Orchestration solutions, while collaborating with Data Scientists. o Enable/Support platform to do distributed data processing using Apache Spark and other distributed / scale technologies. o Build ETL pipelines, ingress / egress methodologies in context to AIML use-cases. o Build highly scalable backend REST APIs for metadata management and other misc. business needs. o Deploy Application in Azure Kubernetes Service using GitLab, Jenkins, Docker, Kubectl, Helm and Manifest o Experience in branching, tagging, and maintaining the versions across different environments in GitLab. o Review code developed by other developers and provide feedback to ensure best practices (e.g., design patterns, accuracy, testability, efficiency etc.) o Work with relevant engineering, operations, business lines, and infrastructure groups to ensure effective architectures and designs and communicate findings clearly to technical and non-technical partners. o Perform functional, benchmark & performance testing and tuning to achieve performant AIML workflow(s), interactive notebook user experiences, and pipelines. o Assess, design & optimize the resources capacities for ML based resource (GPU) intensive workloads. o Communicate processes and results of the application with all parties involved in the product team, like engineers, product owner, scrum master and third-party vendors. Top 5-10 responsibilities for this position: o Experience developing AIML platforms & frameworks (including core offerings such as model training, inferencing, distributed/parallel programming), preferably on Kubernetes and native cloud. o Highly skilled with Python or JAVA programming languages o Highly skilled with database languages like SQL & NoSQL o Experience designing, developing, and deploying highly maintainable, extensible, and testable distributed applications using Python and other languages. o Experience developing ETL pipelines and REST APIs in Python using Flask or Django o Experienced with technologies/frameworks including Kubernetes, Helm Charts, Notebooks, Workflow orchestration tools, and CI/CD & monitoring frameworks. Basic Qualifications: Bachelor's/master's degree in computer science or data science 9+ years of experience in software development and with data structures/algorithms Required Technical Qualifications / Skills: Experience with AI/ML open-source projects in large datasets using Jupyter, Argo, Spark, Pytorch, TensorFlow Experience creating Unit and Functional test cases using PyTest, UnitTest Experience with training and tuning models in Machine Learning Experience working with Jupyter Hub Experience with DB management system like PostgreSQL Experience in searching, monitoring, and analyzing logs using Splunk/Kibana GraphQL/Swagger implementation knowledge Strong understanding and experience with Kubernetes for availability and scalability of applications in Azure Kubernetes Service Experience building CI/CD pipelines using Cloudbees Jenkins, Docker, Artifactory, Kubernetes, Helm Charts and Gitlab Experience with tools like Jupyter Hub, Kubeflow, MLFlow, TensorFlow, Scikit, Apache Spark, Kafka Experience with workflow orchestration tools such as Apache Airflow, Argo workflows Familiarity with Conda, PyPi, and Node.js package builds Preferred Qualifications / Skills: Experience with AI/ML open-source projects in large datasets using Jupyter, Argo, Spark, Pytorch, TensorFlow Experience creating Unit and Functional test cases using PyTest, UnitTest Experience with training and tuning models in Machine Learning Experience working with Jupyter Hub Experience with DB management system like PostgreSQL Experience in searching, monitoring, and analyzing logs using Splunk/Kibana GraphQL/Swagger implementation knowledge Strong understanding and experience with Kubernetes for availability and scalability of applications in Azure Kubernetes Service Experience building CI/CD pipelines using Cloudbees Jenkins, Docker, Artifactory, Kubernetes, Helm Charts and Gitlab Experience with tools like Jupyter Hub, Kubeflow, MLFlow, TensorFlow, Scikit, Apache Spark, Kafka Experience with workflow orchestration tools such as Apache Airflow, Argo workflows Familiarity with Conda, PyPi, and Node.js package builds Referrals appreciated and welcomed. Thanks & Regards, Nora West Recruiter W: +1 (469) 287-8417 E: [email protected] A: 1701 Legacy Dr, Suite#1000, Frisco, Texas - 75034 Keywords: continuous integration continuous deployment artificial intelligence machine learning javascript database California |
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| Fri Nov 03 22:35:00 UTC 2023 |