• Build new AI security features into our access proxy product, the point where agent and user traffic is inspected and controlled. • Treat protocols such as Model Context Protocol (MCP) as enforcement boundaries, and build controls for agent-to-agent communication. • Connect that product to the Delinea Platform and prove the experience with design and paying customers on live capability. • Move those features into the Delinea Platform as the product matures, keeping customers running smoothly throughout the transition. • Enforce just-in-time access and policy decisions in the request path, drawing on the platform's identity and authorization building blocks. • Partner with product and with the teams building the platform's proxy and access engine.

• Build AI agent discovery on top of Delinea's existing cloud discovery rather than standing up a separate inventory, covering Microsoft Azure today and expanding to AWS and Google Cloud as the platform adds those clouds. • Build discovery for AI agents running on endpoints, a newer surface for the team. • Assess the security posture of discovered agents: how they are configured, what they can access, and where they create risk. • Feed discovered agents and their context into our identity and attribution layer so they can be governed. • Partner closely with the platform teams whose discovery services your work builds on.

• Build and maintain full-stack web and mobile-friendly applications, including React-based front ends and their supporting data layers. • Design and maintain Airtable bases (schema, automations, interfaces, and data quality) that back internal and client-facing tools. • Build workflow automations in n8n and Zapier that connect the CRM, forms, databases, and other services. • Integrate third-party and AI services (for example, LLM APIs such as Anthropic's Claude) into applications and workflows where they add value. • Test, debug, and optimize applications for performance, reliability, and usability. • Collaborate with leadership, the Tech Project Manager, and external vendors to scope work and ship in tight, disciplined cycles. • Document systems and establish maintainable patterns so no single tool depends on one person.

Role Description We are looking for a highly skilled Machine Learning Engineer with strong expertise in both traditional machine learning and modern Generative AI (LLMs) to design, build, and deploy scalable AI solutions. The ideal candidate will work on large-scale structured and unstructured data, enabling intelligent automation and insights, particularly in domains such as insurance (claims, underwriting, fraud detection). Qualifications - Strong foundation in machine learning algorithms (supervised & unsupervised) - Experience with anomaly detection, time-series, and predictive modeling - Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow) - Hands-on experience with LLMs (OpenAI, Claude, Hugging Face, etc.) Requirements - Strong understanding of RAG (Retrieval-Augmented Generation) - Prompt engineering & evaluation - Embeddings & vector databases (e.g., FAISS, Milvus) - Experience building GenAI applications (chatbots, document summarization systems) - Experience working with large datasets (batch + streaming) - Knowledge of data pipelines and tools (Spark, Airflow, or similar) - Familiarity with cloud platforms (Azure, AWS, or GCP) - Experience deploying models using APIs (FastAPI/Flask) - Understanding of Docker, CI/CD pipelines, and model monitoring - Knowledge of version control and experiment tracking tools Benefits - Lead large-scale AWS-to-GCP cloud transformation initiatives. - Work on enterprise Data Lakehouse and analytics modernization projects. - Opportunity to migrate enterprise BI platforms from Qlik Sense to Looker. - Flexible remote work environment. - Exposure to global enterprise customers. - Collaborative, innovation-driven engineering culture. - Continuous learning and certification opportunities.