Senior SW Engineer, AI Automation

Dentsply Sirona, Inc•Madison, WI

2d•$176,000 - $198,000

About The Position

Dentsply Sirona's software engineering and cloud operations team is seeking a Senior Software Engineer II to focus on AI-driven automation across one or more products, services, or platforms. This is a senior individual contributor role combining hands-on software engineering, technical expertise, and architectural influence. You will design, build, and operate AI-powered automation capabilities, collaborate across teams, and help drive responsible adoption of AI technologies through engineering best practices, governance, and operational excellence.

Requirements

BS or MS in computer science or a related engineering discipline.
8+ years as a Software Engineer, Full Stack Engineer or comparable role.
Demonstrated experience leading complex technical initiatives, driving architecture decisions, and delivering production-grade software systems.
Proven track record designing and delivering production-grade systems and driving technical excellence across projects.
Hands-on experience integrating AI components (RAG, embeddings/vector DBs, light agents, MCP-style integrations) into engineering workflows.
Experience with distributed systems, APIs and service-level design.
Experience contributing to operational readiness, incident response, and service reliability practices.
Proven pragmatic system and service design skills at program scope.
Deep understanding of scalability, performance optimization and cost trade‑offs for AI and platform services.
Practical experience with cloud platforms (AWS/Azure/GCP), containerization (Docker) and exposure to Kubernetes.
Experience with CI/CD tooling (GitHub Actions/Jenkins/GitLab), feature-flagging, and Infrastructure-as-Code (Terraform/ARM).
Working knowledge of RAG pipelines, embedding stores/vector DBs, prompt engineering and retrieval-quality evaluation.
Strong testing and observability discipline: unit/integration tests, logging, metrics and distributed tracing.
Awareness of data privacy, access controls and secure handling of embeddings/logs.
Strong collaboration, stakeholder engagement, and communication skills with the ability to influence technical decisions across teams.
Excellent English communication skills (written and spoken); German advantageous.

Nice To Haves

Experience integrating AI checks into CI/CD (policy checks, automated test generation, documentation synthesis).
Familiarity with model evaluation, offline/online metrics and A/B testing for ML features.
Experience with MCP or similar context-sharing protocols.
Regulated-industry (medical device) experience is a plus.

Responsibilities

Lead the technical design and implementation of AI automation initiatives within one or more epics or projects.
Collaborate with Product, UX, QA, Security, and Cloud Operations teams to deliver high-quality solutions.
Influence technical direction through architecture discussions, design reviews, and engineering best practices.
Mentor and support engineers through code reviews, technical guidance, and knowledge sharing.
Design and implement AI-enabled automation workflows for parts of the software development lifecycle (requirements/story generation, documentation scaffolding, code scaffolding) within the program area.
Design and implement RAG pipelines, lightweight intelligent agents, and MCP or equivalent integrations to support engineering workflows.
Integrate AI-assisted code generation and validation into CI/CD with guardrails, automated checks, feature flags, staged rollouts and safe rollback conditions.
Ensure human-in-the-loop checkpoints, prompt and policy guardrails, provenance/traceability, and least-privilege access controls for embeddings and logs.
Prepare runbooks, dashboards and alerts; participate in incident responses and contribute to post-incident reviews for owned services.
Monitor and optimize reliability, latency, correctness and cost of AI features; manage model drift and retrieval quality metrics.
Continuously evaluate and integrate emerging AI technologies to improve developer productivity, quality and efficiency.
Define and enforce team-level AI governance: data handling policies, retention, access control and regulatory traceability where required.
Establish detection of metrics and validation processes to detect hallucinations, data leakage and model drift; automate tests where feasible.
Ensure delivered features include automated tests, documentation, and observability (logs, metrics, traces).