Artificial Intelligence/Machine (AI/ML) Learning Engineer

The Artificial Intelligence/Machine (AI/ML) Learning Engineer plays a vital role in designing, building, and implementing enterprise-grade Artificial Intelligence and Machine Learning solutions. This position focuses on leveraging the Microsoft and Azure stack to create and deploy intelligent automation, predictive analytics, and other AI-powered tools. This role will collaborate closely with business units to gather requirements and scope projects, and drive them to resolution, while also helping to establish and build out the company’s AI Center of Excellence (COE)., Essential Duties and Responsibilities include the following. Other duties may be assigned.

• Design and implement end-to-end AI and Machine Learning solutions, platforms, and tools.
• Develop solutions focused on Intelligent Automation and Predictive Analytics.
• Build and deploy models predominantly within the Microsoft and Azure Stack (e.g., Azure ML, Cognitive Services, Azure Databricks).
• Leverage the Google Vertex AI Stack(e.g., BigQuery, VertexAI, Imagen, Veo, Gemini API and Agentic Platform) where the toolset meets the business need.
• Collaborate with business stakeholders to gather user requirements, define project scope, and translate needs into technical specifications.
• Assist in the development, documentation, and governance of the AI Center of Excellence (COE).
• Drive AI/ML projects from initial conception and design through to deployment, monitoring, and resolution.
• Design and conduct experiments and tests with a focus on model performance, scalability, and accuracy.
• Collaborate with data engineers, developers, and other IT personnel to design and integrate compatible solutions.
• Evaluate existing systems and provide advice on integrating new AI/ML capabilities.
• Stay current with the latest advancements in AI, machine learning, and the Azure ecosystem.

Education/Experience: Bachelor’s degree (B. S.) or equivalent from four-year college in Computer Science, Data Science, Artificial Intelligence, or related field preferred. 3-5+ years of hands-on experience in data science, machine learning, or AI engineering. Demonstrable experience with the Microsoft and Azure AI stack (e.g., Azure ML, Azure Cognitive Services, Azure Databricks, Power BI). Equivalent combination of education and experience will be considered.

Computer Skills: To perform this job successfully, an individual must have the following core capabilities. Proficiency in programming languages such as Python or R. Deep experience with the Microsoft Azure AI stack: Azure Machine Learning, Cognitive Services, Azure Databricks, Azure Data Factory. Experience with ML frameworks and libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Strong understanding of data modeling, data warehousing, and predictive analytics. Knowledge of MLOps principles and tools for model deployment and management.

Certificates and Licenses: CCNA / CCNP preferred but not required.

The hiring pay range provides a good faith estimate of the salary or hourly wage that Living Spaces expects to pay for the position upon hire. Pay will be determined by several factors, including, but not limited to: applicant’s education, relevant work experience, knowledge, applicable and relevant skills and abilities, market demand, Company budget, as well as internal equity and alignment with geographic market data. Living Spaces reserves the right to modify this page at any time.

Compensation: -

Overtime pay is available for eligible, non-exempt Team Members. Reimbursement for expenses as required by applicable law and Company policy. Retail, Guest Services and Distribution Center Team Members are eligible to receive team bonus based on meeting specific goals and KPI’s.

Additional available benefits upon meeting eligibility requirements include:

• Medical (full-time only)
• Dental (full-time only)
• Vision (full-time only)
• 401(k) with Company match (full and part-time)
• Vacation (full-time only or as otherwise required by applicable law)
• Paid Sick Leave (full and part-time)
• Flex or Health Spending Account (for eligible full-time only)
• Employee Assistance Program (full and part-time)
• Holiday pay (full-time only)
• Life insurance (full-time only)