Data Engineer (GenAI)

Please see the high-level pointers:

1.Proficiency in cloud-native architectures and services on both Azure and AWS.

2.Track record of leading end-to-end delivery of generative AI projects or AI-powered platforms.

3.Ability to translate business requirements into technical solutions and manage delivery timelines.

4.Experience coordinating cross-functional teams including developers, cloud engineers, and possibly data scientists.

5.Experience mentoring and providing technical guidance to developers and engineers.

6.Knowledge of cloud security best practices, identity and access management, and data privacy considerations relevant to AI workloads.

7.Ability to troubleshoot complex technical issues related to AI model deployment and cloud infrastructure.

Education and Experience:

•Degree/Master’s in Computer Science, Information Technology,

Computer Engineering, or equivalent.

•Minimum of 10 years of experience in data analytics, with at least 5

years in a management role managing large-scale data analytics

projects.

•Proven experience with AWS cloud services, including setting up and

managing cloud infrastructure, and deep expertise in Databricks

Lakehouse and AI/ML technologies.

Technical Skills:

•Design, develop, and implement GenAI solutions that integrate with

Azure and OpenAI platforms.

•Conduct research and stay up-to-date with the latest advancements

in GenAI, Azure, OpenAI, AWS, and Google GenAI technologies.

* Experience with AWS services, such as Amazon SageMaker, AWS

Lambda, and AWS AI/ML services.

* Knowledge of Google Cloud services, including Google Cloud AI

Platform, Google Cloud Functions, and Google Cloud AutoML.

* Experience with cloud-based deployment and scaling of GenAI

applications on Azure, AWS, and Google Cloud.

•Proficiency in databases (e.g., Oracle, MS SQL, MySQL, Teradata,

Databricks), data repositories (e.g., data lakes, data marts).

•Experience in data modelling, data transformation, and statistical

computing (e.g., R, Python).

•Deep understanding of machine learning (MLOps), data visualization

(e.g., Power BI, Tableau), and event-driven architecture.

•Expertise in cloud-native technologies and services, including

microservices, serverless computing, and containerization (e.g.,

Docker, Kubernetes).

•Strong knowledge of cloud data platforms, including architecture

design, data integration, and the implementation of scalable data

pipelines.

“By proceeding with the job application, you are deemed to have read and acknowledged our Job Applicant Privacy Policy and consented to us using the personal data you shared for the purpose stated in the said policy.”