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Vertex AI is an advanced platform from Google Cloud that facilitates the work of data scientists and machine learning (ML) engineers by simplifying the process of training models and managing ML projects. The demand for AI-related positions has seen a significant growth, particularly in fields related to ML and generative AI (GenAI). Against this backdrop, companies across sectors are seen posting jobs related to Vertex AI, finds GlobalData, a leading data and analytics company.
Sherla Sriprada, Business Fundamentals Analyst at GlobalData, comments: “As the demand for AI-driven solutions continues to surge, companies are prioritizing Vertex AI in specialized fields such as ML, data engineering, and AI ethics. This shift is reshaping hiring patterns, emphasizing candidates with specialized expertise in Vertex AI to drive their initiatives forward.”
An analysis of GlobalData’s Job Analytics Database reveals an increase in job postings focused on Vertex AI across sectors including technology, retail, financial services, and pharma.
Technology industry is focusing on initiatives such as designing, implementing, and deploying AI/ML solutions on the google cloud platform (GCP), particularly using vertex AI. Additionally, it involves leading product strategy for Gemini’s coding capabilities, developing no-code/low-code agent builders, and establishing quality standards while integrating third-party APIs.
Technology companies are seeking to hire professionals with experience in building advanced analytics frameworks using tools like bigquery ML and tensorflow, as well as designing and maintaining data pipelines with GCP services such as dataflow and Vertex AI.
Financial services industry is seeking to hire professionals with experience in AI/ML to design and implement secure, high-performing end-to-end data solutions for fintech initiatives that translates business problems in the financial domain into technical solutions that leverage machine learning. Firms are also focused on developing and testing AI models using Vertex AI and BQML to generate insights from both structured and unstructured data.
Retail companies are looking at initiatives such as automating data jobs on GCP and utilizing services such as bigquery, dataflow, dataproc, apache airflow, and Vertex AI pipelines. This approach aims to establish a reliable and scalable data infrastructure for business consumption. Furthermore, it involves analyzing large-scale datasets and developing statistical models with Vertex AI to generate actionable insights that promote operational efficiency, revenue growth, and improved customer satisfaction.
Pharma companies are exploring scalable generative AI solutions for various applications, including drug discovery, medical writing automation, clinical trials, regulatory submissions, and real-world evidence generation. Additionally, these companies are seeking experience in front-end API development using fastAPI and cloud AI services such as AWS Bedrock, Azure OpenAI, and google Vertex AI.
Sriprada concludes: “As organizations continue to embed AI across core functions, the demand for professionals is set to rise. This trend underscores a broader industry pivot toward scalable, secure, and efficient AI deployment frameworks that accelerate innovation and competitive differentiation.”