AI Architect

What success looks like in this role:

The key responsibilities of an Architect position can vary depending on the specific industry, organization, and project requirements. However, some common responsibilities typically associated with an Architect role include:

1. Architecting Data Solutions : Designing and architecting end-to-end data science solutions that meet business objectives. This involves understanding the business requirements, data infrastructure, and available technologies to create scalable and efficient data architectures. Agentic AI, AI Agent, Azure Foundry, AWS, Cloud

2. Data Modeling and Analysis : Developing data models and algorithms to analyze complex datasets, extract insights, and make >

You will be successful in this role if you have:

• BA/BS degree and 13+ years’ relevant experience OR equivalent combination of education and experience

• Master’s degree preferred

The qualifications required for a Data Science(AI) Architect position typically span a combination of technical skills, domain expertise, and soft skills. Here are the key qualifications:

• Advanced Data Science Skills : Proficiency in data science concepts, methodologies, and tools, including machine learning, statistical analysis, data mining, and predictive modeling. Strong programming skills in languages such as Python, R, or Scala are often essential.

• Data Engineering Expertise : Deep understanding of data engineering principles, including data integration, data pipelines, ETL processes, and data warehousing. Experience with big data technologies such as Hadoop, Spark, and Kafka is valuable.

• Data Architecture and Design : Experience in designing scalable and efficient data architectures to support data science initiatives. Knowledge of database technologies, data modeling techniques, and cloud platforms such as AWS, Azure, or Google Cloud is essential.

• Software Development Skills : Proficiency in software development practices, including version control, testing, and debugging. Experience with software engineering tools and frameworks such as Git, Docker, and Kubernetes is beneficial.

• Domain Knowledge : Understanding of the industry or domain in which the organization operates, including relevant business processes, data sources, and regulatory requirements. Domain expertise helps in identifying relevant use cases and designing effective data solutions.

Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.

Local employment practices and rights may vary by jurisdiction and are subject to applicable local laws. This commitment includes our efforts to provide for all those who seek to express interest in employment the opportunity to participate without barriers.

If you are a US job seeker unable to review the job opportunities herein, or cannot otherwise complete your expression of interest, without additional assistance and would like to discuss a request for reasonable accommodation, please contact our Global Recruiting organization at GlobalRecruiting@unisys.com. US job seekers can find more information about Unisys’ EEO commitment here.