Principal Applied AI Engineer (AI)

Location: Redmond, Washington, United States; 3 days per week in-office

Salary: USD $142,800–$274,800 per year across the U.S.; USD $188,000–$304,200 per year in the San Francisco Bay Area and New York City metropolitan area.

Company

hirify.global is presented in the posting as part of Microsoft’s Dynamics 365 ecosystem, delivering enterprise SaaS solutions for intelligent customer service operations.

What you will do

• Own the architecture, strategy, and execution of AI-powered features across Dynamics 365 Sales and Service.
• Design and deliver production-ready solutions using LLMs, natural language understanding, speech, and real-time reasoning.
• Lead initiatives covering AI model integration, scalability, reliability, maintainability, and cloud-scale deployment.
• Partner with engineering, product, design, applied science, data science, and infrastructure teams to translate customer needs into intelligent capabilities.
• Establish engineering standards for code quality, observability, testing, MLOps, and secure deployment.
• Mentor engineers, evaluate emerging AI frameworks and tools, and advise on cross-team architectural planning.

Requirements

• Bachelor’s degree in Computer Science or a related technical field plus 6+ years of technical engineering experience, or equivalent experience.
• At least 2 years of experience delivering AI/ML systems at production scale, ideally involving LLMs, transformers, RAG pipelines, or similar architectures.
• Experience leading engineering teams or cross-functional initiatives involving AI systems in cloud environments.
• Hands-on coding experience with languages such as C, C++, C#, Java, JavaScript, or Python.
• Ability to meet Microsoft, customer, and government security screening requirements, including the Microsoft Cloud Background Check.

Nice to have

• 8+ years of hands-on software engineering experience with C#, Python, Java, or equivalent.
• Experience designing and deploying AI-first applications at scale in enterprise SaaS, including performance, privacy, compliance, and operational considerations.
• Expertise in MLOps/LLMOps, model versioning, retraining pipelines, A/B testing, monitoring, and rollout strategies.
• Experience with Azure, Kubernetes, Docker, Azure AI, OpenAI, HuggingFace, or custom-trained models.
• Experience with customer service, CRM, enterprise productivity, Zero Trust, or highly regulated environments.

Culture & Benefits

• Work on AI-native customer service capabilities used by large enterprises worldwide.
• Collaborate across engineering, product, design, data science, infrastructure, and applied science functions.
• Contribute to a culture of innovation, inclusivity, mentoring, and continuous learning.
• Benefits and additional compensation may be available depending on the role and work location.