Senior AI Engineer (C#
Job description
About the role
The Senior AI Engineer (C#) is entrusted with owning the complete lifecycle of AI features from initial conception through production deployment. This role translates high-level product strategy into robust, scalable systems that are delivered reliably and safely. The ideal candidate operates as a fluent C# architect who balances delivery speed with rigorous data safety and clear communication under demanding conditions. They serve as the critical bridge between product intuition and engineering reality, navigating distinct operational layers with pragmatic problem-solving. Collaboration is central, as this position guides teams and elevates the technical discourse across the organization. The work demands a balance of innovation and pragmatism to ship intelligent features that drive tangible business value. Ultimately, the engineer is accountable for advancing the capabilities of AI within the product while maintaining the highest standards of reliability and security.
Key facts
What you'll do
- Architect sophisticated agent structures using C# and ASP.NET Core designed for seamless consumption by React frontends.
- Guide cross-functional teams as a technical leader, providing direction and mentorship on complex implementation challenges.
- Design and implement resilient workflows that facilitate code creation, automated verification, and streamlined CI/CD processes across .NET and JavaScript repositories hosted within Google Cloud Platform.
- Construct secure data pipelines dedicated to the ingestion and retrieval of internal records, enabling context-aware enterprise awareness.
- Ship guarded, predictive features to external clients while maintaining strict isolation and privacy for all accounts and data interactions.
- Establish comprehensive deployment, monitoring, and cost control rules for agentic workloads deployed on Google Cloud Platform across multiple engineering teams.
- Review and recommend model choices while mentoring engineers on effective agent patterns, optimal tool usage, and principles of system reliability.
- Build robust connectors to external services utilizing tool calling and protocol servers to enable safe and efficient automation.
- Advance evaluation methodologies and reliability practices for AI components throughout their entire lifecycle, from development to production.
- Champion the adoption of .NET-native AI toolkits to ensure alignment with the broader technology stack and strategic objectives.
Requirements
You must possess six or more years of professional software engineering experience demonstrating a deep and broad skill set. Strong proficiency in C# and the .NET ecosystem, including ASP.NET Core, is a non-negotiable requirement for this position. You must show a track record of two or more years creating production-grade systems that integrate with Large Language Models. This experience must include advanced prompting techniques and sophisticated function calling implementations. A history of shipping agentic or intricate LLM systems at scale is essential, requiring a solid understanding of retrieval mechanisms, vector stores, and context management strategies. Hands-on experience utilizing AI agents within development workflows is mandatory to succeed in this environment. Familiarity with .NET-native AI toolkits is not optional but essential for effective performance. You must demonstrate solid operational experience on Google Cloud Platform, specifically with services such as GKE, Cloud Run, Pub/Sub, and Cloud Storage. Expertise in API design patterns, distributed logic, asynchronous programming patterns, and multi-client security isolation is required. Clear communication skills, the ability to collaborate closely with diverse stakeholders, and a strong sense of ownership for outcomes affecting products and colleagues are mandatory.
Nice to have
Experience with Vertex AI and the broader GCP AI and ML ecosystem is considered a significant advantage for this role. An understanding of cross-language interoperability is beneficial, as AI ecosystems frequently leverage Python alongside other languages. Knowledge of observability and assessment tools such as Cloud Monitoring and OpenTelemetry, combined with familiarity with AI safety methods, is viewed as beneficial for enhancing system robustness.
Practical notes
This position requires adherence to the specified working hours as outlined in the practical notes of the job description. Travel requirements and visa sponsorship details, if applicable, must be consulted in the official offer documentation.