Principal Applied AI Engineer

As a Principal Engineer, you will: Design and deliver production-ready AI solutions that leverage large language models (LLMs), natural language understanding, speech, and real-time reasoning to improve agent productivity and customer satisfaction. Lead complex technical initiatives, including AI model integration, platform scalability, reliability, and long-term maintainability. Collaborate deeply with applied scientists, product managers, and UX teams to translate customer needs into intelligent capabilities that deliver measurable business value. Drive engineering rigor across the team by establishing high standards for code quality, observability, testing, MLOps, and secure deployment practices. Mentor engineers across levels, fostering a culture of innovation, inclusivity, and continuous learning. Proactively identify technology gaps, evaluate emerging AI frameworks/tools (including open-source and Azure AI offerings), and champion adoption where appropriate. Act as a technical advisor across the broader organization, contributing to cross-team initiatives and long-term architectural planning. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 2+ years of experience delivering AI/ML-based systems at production scale, ideally including LLMs, transformers, RAG pipelines, or similar architectures. Demonstrated experience leading engineering teams or cross-functional initiatives involving AI systems in cloud environments. 8+ years of hands-on software engineering experience with deep expertise in languages such as C#, Python, Java, or equivalent. Proven track record of designing and deploying AI-first applications at scale, with deep understanding of performance, privacy, compliance, and operational constraints in enterprise SaaS. Expertise in MLOps/LLMOps, including model versioning, retraining pipelines, A/B testing, monitoring, and rollout strategies. Deep knowledge of cloud platforms (preferably Azure) and experience deploying containerized AI services using Kubernetes, Docker, or similar. Hands-on experience integrating models from Azure AI, OpenAI, HuggingFace, or custom-trained models into scalable application pipelines. Strong architectural and systems thinking—capable of making trade-offs between performance, cost, simplicity, and maintainability. Exceptional written and verbal communication skills with the ability to influence across roles and levels. Experience working in highly regulated or secure environments, including Zero Trust, privacy, and compliance practices. Prior experience working with or building solutions for customer service, CRM, or enterprise productivity scenarios is a strong plus.