Barbara Burns’ Post

Machine Learning Engineer/AI Engineer II Saxon Global Job Description: 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,... See the full job description on illbeback: https://lnkd.in/dE9Fkj2h #AI #ML #AIJobs #AITalent #hiring #MachineLearning #N/A

I have a hot tip for the industry! A senior "data engineer" with experience in your cloud provider of choice is absolutely a viable choice for an "ML engineer" role. Personally, I remember getting into data engineering partially as a stepping stone to get into a role that involved AI/ML. Now, as I look into both data and ml engineer roles, I see much less data and much more ml but the requirements are ridiculous. Where are these "ml engineers" supposed to come from? How many people do you think actually have 10+ years experience as an "ML engineer"? Did you ever hear the term "ml engineer" 10 years ago? Side note: I know where to find a whole bunch of engineers with experience with data lakehouses, GCP, airflow, dbt, marketing data, and the media industry. We were all dropped on New Years day and are trying to navigate a job market with some serious growing pains. #technology #ml #machinelearning  #ai #artificialintelligence #selftaught #techindustry #proofofconcept #proofofcompetency  #recruitment #engineer #gcp #airflow #vertexai #aiplatform #dbt

INCORPORAN INC is looking for Machine Learning - AI Architect for our client in the location is Remote   Please send me your resumes and contact details at rajeshkumar@incorporaninc.com or call Rajesh at 609-474-4722   Job Description:   Required skills: AWS, Python, Airflow, Kedro, or Luigi Hadoop, Spark, or similar frameworks. Experience with graph databases a plus. 1.           Designing Cloud Architecture: As an AWS Cloud Architect, you’ll be responsible for designing cloud architectures, preferably on AWS, Azure, or multi-cloud environments. Your architecture design should enable seamless scalability, flexibility, and efficient resource utilization for MLOps implementations.   2.           Data Pipeline Design: Develop data taxonomy and data pipeline designs to ensure efficient data management, processing, and utilization across the AI/ML platform. These pipelines are critical for ingesting, transforming, and serving data to machine learning models.   3.           MLOps Implementation: Collaborate with data scientists, engineers, and DevOps teams to implement MLOps best practices. This involves setting up continuous integration and continuous deployment (CI/CD) pipelines for model training, deployment, and monitoring.   4.           Infrastructure as Code (IaC): Use tools like AWS CloudFormation or Terraform to define and provision infrastructure resources. Infrastructure as Code allows you to manage your cloud resources programmatically, ensuring consistency and reproducibility.   5.           Security and Compliance: Ensure that the MLOps architecture adheres to security best practices and compliance requirements. Implement access controls, encryption, and monitoring to protect sensitive data and models.   6.           Performance Optimization: Optimize cloud resources for cost-effectiveness and performance. Consider factors like auto-scaling, load balancing, and efficient use of compute resources.   7.           Monitoring and Troubleshooting: Set up monitoring and alerting for the MLOps infrastructure. Be prepared to troubleshoot issues related to infrastructure, data pipelines, and model deployments.   8.           Collaboration and Communication: Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders. Effective communication is essential to align technical decisions with business goals. 

Staff MLOps Engineer, Data Platform https://ift.tt/5wXFmpq via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 February 04, 2023 at 11:36AM . . . #dataengineer #datascience #python #datascientist #data #dataengineering #dataanalytics #bigdata #cat #machinelearning #dataanalysis #pythonprogramming #artificialintelligence #hadoop #devops #deeplearning #bigdataanalytics #datasciencetraining #cloudcomputing #datavisualization #computerscience #coding #dataanalyst #programminglife #bigdatatechnologies #ai #sql #pythonlearning #nosql #rprogramming

#hiringalert 🔸 Position - Senior Software Engineer 🔸   The role involves developing a full stack analytic system including workflow/process graph compilations, workflow orchestration, distributed process execution, distributed file storage and system level performance optimization with TF/Torch/Cuda and other computing acceleration libraries, and developing an api server and visualisation utilities to interact with the HPC backend. The Analytic Engine will be used in a highly scaled national level Geospatial Engine storing and processing large data such as satellite imagery. Good to see on resume: 🔹 First hand experience in MLOps on providers such as databricks, AWS (Sagemaker), Azure, Kubeflow, etc. 🔹First hand experience with writing optimized Python code using libraries like asyncio, dask, etc. 🔹Experience with technicalities of data visualization tools such a superset (internals, code modification, etc) and API servers for data science based applications. 🔹First hand experience building self hosted analytic clusters with Dask or Spark. 🔹Good experience in optimizing ML workflows on Pytorch / TF with system level improvements such as prefetching, shared data, Gpu Direct Memory, etc would bump up your visibility. 🔹Experience developing software in Cloud Native approach will also bump up your visibility, for e.g exposure to Kubernetes (CRDs, Operators), Istio, Argo, NATS, etc You can mail your resumes to - jayashree.viswanathan@datakaveri.org #HPC #DataAnalytics #IUDX #CDPG #HighPerformanceComputing #MachineLearning #DataVisualization #PythonCoding #CloudComputing #SoftwareEngineering #DistributedSystems #MLoptimization #BigData #APIdevelopment #DataScience #MLOps #CloudNative #CareerOpportunity #ProfessionalGrowth #JobOpening #SeniorSofwareEngineer #TechCareers #NowHiring #centreofdataforpublicgood 

Day 2 of #mlops Roles in MLOps - Business Roles - Technical Roles 1. Business Roles - B/S stakeholders ~ Budget decisions ~ Vision of the company ~ Involved throughout the lifecycle - Subject matter expert ~ They have domain knowledge ~ Involved throughout the lifecycle ~ Interpret and validate data 2. Technical Roles - Data Engineer ~ Collecting, storing and processing data ~ Check and maintain data quality - Data Scientist ~ Data Analysis ~ Model training and evaluation - Software Engineer ~ Write software for model deployment ~ Make sure that code follows guidelines - ML Engineer ~ Versatile role ~ Designed for the complete ML lifecycle - Backend Engineer ~ Enable development and deployment ~ Cloud Infrastructure #datascientists #machinelearning #machinelearningengineer #algorithms #artificialintelligence #data #dataanalytics #dataengineering #dataengineer #softwaredevelopment #softwaredesign

(Hiring - MosaicML/Databricks GenAI Inference) 1/ Foundational Models (FMs) will change the way Human-Software, Software-Software, Human-Human interactions work. 2/ Open Source Software (OSS) (models and tools) is going to be critical component for truly democratizing adoption of FMs in the Enterprises. 3/ OSS models and tools are useful in Enterprises, only when there are enterprise grade software (secure, reliable, scalable, performant and well integrated with ecosystem) suite offerings that enable adoption of OSS. We at MosaicML (DataBricks) are working towards building the enterprise ready software ecosystem (FMs, model training, fine-tuning, serving products, and more). My team (MosaicML/DataBricks foundational model serving) is hiring. If you are passionate about innovating model serving (inference) software stack - hardware optimization, model optimization, model server, scaling serving infrastructure with Kubernetes, this would be a fantastic opportunity to join our ambitious, innovative, fast moving team at MosaicML (DataBricks). Why I think you will be excited to join us? 1/ We believe in the power of OSS. We believe enterprise grade software ecosystem is critical for the success of OSS in GenAI. We are looking forward you joining our hands in this mission. 2/ We don’t just build infrastructure. We are neck deep in the core fundamentals - our teams at MosaicML builds MPT model (Mosaic Pre-Trained Models). Our MPT model, Composer Library are all open source too! 3/ DataBricks Datal Lake (overall data infrastructure) will enable us to deliver the power and value of GenAI to Enterprises, where Enterprises are today with their data. 4/ Broadly, model training (pre-training, fine-tuning) and model serving are two pillars of GenAI. As GenAI will continue to have massive adoption across products, you will be developing state-of-the-art (SOTA) technology and expertise for model serving, making you as a critical person in the field. 5/ You will be working with researchers and engineers at Mosaic, as well as collaborate with OSS community, building SOTA techniques in the space; 6/ As one of the founding engineer in the team, you will experience and play critical role in idea to scale growth of team/culture/product/strategy/technology. Some of the problems we are working are: 1/ Which accelerator to use for a given model? Can we abstract away this from the user? 2/ How to efficiently serve multiple models? 3/ How to efficiently serve Mixture of Experts(MoE) models, Adapter based fine-tuned models - fast, cheap and easy. 4/ Improve hardware utilization - continuous batching, optimized kernels etc. Server - vLLM, TRT-LLM optimizations 5/ How to serve vision foundational models (ex: stable diffusion) efficiently? 6/ Scaling K8s infrastructure - security, scalability and cost efficiency And many more… Please DM me for more details or apply on the role (links in comment below). Please share and help spread the word!

Nowadays the definition of Data Scientist/Data Science Specialist has changed, when companies are writing job role as Data Scientist, they actually mean Data Scientist + DevOps/MLOps Engineer + Full Stack Developer + Big Data Engineer(sometimes) + Data Engineer(sometimes) + Generative AI Specialist … you might not need to be a pro on Data Engineering or Gen AI or Application Development but you should have some idea behind it, plus you will need to have expertise on atleast 2 cloud platforms(preferably Azure and AWS - because of large market share if these 2), So my advice to the newbies stepping into Data Science field is that rather than spending time on Mathematical Intuitions behind the ML/DL methods, Algebra, Calculus or deep dive on statistics, look for courses which have or learn MLOps CI/CD Pipeline implementation, Some concepts of Gen AI and its implementation, at least 2 cloud platforms, because you can survive in Data Science without knowledge of Deep Mathematical intuitions - only you need to have the basics overview cleared and should know to implement it through coding, but you won’t be able to survive without having knowledge on the mentioned things. I know there are overlaps in the skillsets required for mentioned roles but you’re expected to have all the skillsets. PS: This is my personal opinion based on the 3 years of grind in Data Science and AI field,interviews,rejections. Others views may vary… #datascience #AI #machinelearning #mlops #devops #bigdata #genAI