• Serve as the lead AI Engineer as part of Data Science & Advanced Analytics. • Act as a liaison between management, technical teams, and stakeholders, communicating decisions and technical information. • Architect, develop, and maintain intelligent AI agents using Copilot Studio and Azure AI (OpenAI, Bot Service, Machine Learning). • Optimize AI agents for performance, scalability, and reliability. • Monitor and evaluate AI solutions, ensuring alignment with business goals. • Build and manage Azure Functions, Logic Apps, and automation workflows. • Orchestrate large language models (LLMs) for enterprise use cases (conversational AI, summarization, decision support). • Document and oversee project tasks, maintain project files, and support client meetings. • Develop and adhere to project timelines, identify process improvements, and implement quality initiatives. • Prepare technical documentation, reports, and deliverables using Microsoft Word and Excel. • Conduct research on AI trends, best practices, and emerging technologies. • Participate in training and educational presentations as required. • Monitor and optimize AI system performance, scalability, and reliability. • Ensure compliance with security, privacy, and governance standards. • Always represent HSAG in a professional manner.

• Apply your knowledge of AI systems and software engineering to develop solutions that directly address and resolve business problems. • Partner with professionals from Data Science and Data Engineering to address complex technical challenges, ensuring that the latest and most effective Data & AI techniques are being utilized. • Take ownership of implementing and optimizing applied AI components, ensuring they meet project needs with high complexity and scale. • Navigate and manipulate generative AI models, including (but not limited to) LLMs, to create prompts and solutions tailored to specific use cases. • Develop and incorporate AI solutions while adhering to industry best practices, including moderation, security, monitoring, and compliance standards. • Understand and properly apply Responsible AI concepts in all the stages of the solution. • Lead the charge in designing, measuring, and evaluating AI model outputs, developing standard and custom metrics to ensure alignment with business objectives. • Translate AI research and PoCs into production-ready features, delivering robust and scalable AI components that integrate seamlessly with larger systems. • Drive the selection and application of appropriate evaluation metrics, ensuring that AI solutions are robust, unbiased, and meet all necessary performance standards.

• Architecting and Implementing Agentic Workflows • Design Autonomous Loops: Build stateful control loops (Perception -> Reasoning -> Action) where agents can interpret clinical data, plan necessary checks, and generate risk assessments. • Conversational Agentic workflow: Chatbot style user experience powered by LLM • GCP Integration: Leverage Vertex AI for model serving and LLM models for reasoning, optimizing for long-context windows to process complex medical history. • RAG & Grounding: Implement "Enterprise Grounding" using Vertex AI Vector Search to ensure all agent outputs are strictly cited against official medical literature (e.g., medical research and guidelines). • Data & Tool Integration • Structured Data Access: Build robust Python tools (Function Calling) that allow agents to securely query BigQuery or other datastores for patient demographics and symptoms. • Security & Compliance: Ensure all AI operations comply with HIPAA/GDPR standards, implementing strict PII masking and data governance within the Google Cloud environment. • System Reliability: Deploy agents on Vertex AI, Cloud Run, or GKE, ensuring low-latency inference and high availability for clinical users. • Agent monitoring: implement effective product environment AI Agent execution monitoring and alert system to ensure SLA, safety and security compliance • Leadership & Strategy • Mentorship: Guide a team of engineers in MLOps best practices (CI/CD for models, evaluation pipelines). • Evaluation: Implement "LLM-as-a-Judge" frameworks to automatically test agent accuracy against "Golden Datasets" of clinical cases.

ZeroFox protects organizations from external threats across the public attack surface, and we're building AI into how the company itself operates, not just what it ships to customers. You'll be the person who makes that real. This is a broad, hands-on engineering role. You'll assess business unit workflows, identify where automation creates real leverage, and build the systems that deliver it, whether that's an agent, a custom tool, an integration, or something simpler. You'll also evaluate whether enterprise data is ready for AI consumption and build the access patterns that close the gap. You'll make the judgment on the right approach for each problem, build it, and see it through to production. Some internal tooling may inform customer-facing capabilities over time, so you'll navigate that boundary as it comes up. What you'll do - Assess business unit workflows and identify where automation, agents, or custom tooling create real leverage - Design and build agents for internal operations: orchestration, tool-calling patterns, escalation paths, and human-in-the-loop gates - Build custom internal tools, integrations, and infrastructure that support business operations across functions - Evaluate enterprise data systems for AI readiness: quality, accessibility, governance, and latency. Build the access patterns that make enterprise data usable by AI systems. - Define the AI application lifecycle for internally-built systems: how artifacts get verified, tested, deployed, and maintained. Establish quality gates between prototype and production. - Define and enforce AI governance practices: agent permissions, escalation paths, decision audit trails, behavioral monitoring, and the boundary between autonomous action and human oversight - Build and operate internal AI infrastructure: self-service capabilities, deployment guardrails, observability, and cost management - Build tools and interfaces that make AI capabilities accessible to non-technical teams, translating business problems into working systems they can use directly