Vocera, now part of Stryker, is seeking a highly experienced and visionary Principal Engineer – AI/ML to lead the architecture, strategy, and technical direction of our AI-powered speech and voice intelligence platforms.

This role serves as the AI/ML Architect for real-time speech, conversational AI, and GenAI-driven clinical communication systems. You will define long-term technical vision, establish scalable AI architecture patterns, and guide engineering teams in delivering reliable, secure, and high-performance AI systems deployed at enterprise scale on Microsoft Azure.

This is a hands-on architectural leadership role requiring deep expertise in speech technologies, modern ML/LLM systems, distributed architecture, and cloud-native AI platforms.

• Define and own the end-to-end AI/ML architecture for speech, voice intelligence, and GenAI platforms.

• Establish scalable patterns for real-time speech processing (low-latency ASR, TTS, streaming pipelines).

• Architect RAG-based systems, LLM orchestration layers, semantic search, and conversational AI frameworks.

• Drive design decisions across model hosting, inference optimization, observability, reliability, and cost efficiency.

• Define evaluation frameworks for model quality, accuracy, bias, hallucination control, and safety.

• Design enterprise-grade, cloud-native AI systems on Microsoft Azure.

• Lead architectural decisions for:

  • Model lifecycle management

  • Multi-model orchestration

  • Feature stores and vector databases

  • High-throughput inference pipelines

  • Secure data handling in healthcare environments

• Ensure systems meet performance SLAs (latency, throughput, error rates) and compliance requirements.

• Drive multi-region scalability and disaster recovery strategy for AI workloads.

• Architect solutions for:

  • Real-time speech-to-text and text-to-speech

  • Domain-adapted ASR models

  • Intent recognition and entity extraction

  • Conversational AI assistants

  • Summarization and contextual intelligence

• Define strategies for handling accents, noisy environments, and healthcare-specific terminology.

• Guide model fine-tuning and adaptation for clinical communication use cases.

• Lead adoption of LLM-based architectures including:

  • RAG pipelines

  • Prompt orchestration frameworks

  • Guardrails and safety layers

  • Evaluation and monitoring systems

• Define best practices for prompt engineering, model benchmarking, and production hardening.

• Drive responsible AI practices including governance, auditability, and compliance.

• Provide architectural guidance across multiple pods and teams.

• Review and approve technical designs impacting AI systems.

• Mentor senior engineers and elevate AI engineering maturity across the organization.

• Partner with Product, UX, Security, DevOps, and Platform teams to align AI capabilities with business strategy.

• Participate in customer and executive discussions to translate technical vision into business value.

• Drive innovation initiatives, patents, and strategic technical investments.

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field.

• 12+ years of experience in software engineering with substantial experience in AI/ML systems.

• 6+ years of experience designing and deploying production-grade AI/ML architectures.

• Deep expertise in speech technologies (ASR/TTS), NLP, and modern LLM systems.

• Strong proficiency in Python and AI frameworks (PyTorch, TensorFlow, etc.).

• Proven experience architecting AI workloads on Microsoft Azure.

• Strong background in distributed systems, backend architecture, APIs, and data pipelines.

• Demonstrated experience leading architecture decisions across multiple teams.

• Hands-on experience with Azure OpenAI, Azure ML, and enterprise LLM deployments.

• Experience designing RAG architectures with vector databases and semantic search.

• Expertise in model evaluation frameworks, ML observability, and performance tuning.

• Experience adapting or fine-tuning speech models for domain-specific use cases.

• Familiarity with LangChain, MLflow, prompt evaluation tooling, and model governance frameworks.

• Deep experience with:

  • Azure OpenAI

  • Azure ML

  • Azure AI Search

  • Azure Functions / Container Apps

  • Kubernetes-based model serving

• Experience with CI/CD for ML systems (MLOps best practices).

• Exposure to AWS or GCP AI services is a plus.

• Experience building secure, compliant systems in regulated environments.

• Understanding of PHI handling, data privacy, and enterprise-grade security controls.