• Manage and evolve our API Gateway integration platforms at enterprise scale • Manage the tooling and support for intelligent automated integration with flexibility and reliability as your mission • Analyze and refine API definition standards and drive support for innovation at scale in our API ecosystem • Guide and mentor junior engineers while setting the bar for operational excellence • Managing and advancing the API integration platforms and related infrastructure components, including software upgrades, performance monitoring, and application development team collaboration • Helping lead platform enhancement efforts that enable flexible and secure integration between internal and external services • Providing guidance and expertise for application service design, data and transaction workflows for scalability, reliability and extensibility • Following infrastructure-as-code best practices (Terraform, Helm, Ansible) for repeatable deployments and environment consistency • Driving operational excellence through on-call rotation support, thorough post-mortem investigation, and implementing solutions that improve resilience • Performing unit testing and complex debugging, and identifying opportunities for automated testing where appropriate • Performing analysis of technical feasibility and solution design, including estimation of work efforts, planning, and backlog organization • Ensuring upgrades, patching, and platform updates are proactively planned and executed without business disruption • Helping set reliability targets and defining operational metrics (availability, latency, error budgets) in line with SRE methodologies • Writing detailed technical documentation for systems, including architectural diagrams, and operational procedures for standardized actions used for maintenance and support

• Design, develop, and enhance core components of Arctic Wolf’s real-time detection and event processing platform. • Build and evolve large-scale distributed systems that process and analyze trillions of events per day in near real-time. • Develop capabilities across event processing systems, detection frameworks, event correlation platforms, and stream-processing infrastructure. • Solve complex engineering challenges related to scalability, reliability, performance, latency, throughput, and cost efficiency in cloud-native environments. • Contribute to the architecture and technical direction of next-generation platform capabilities. • Deliver high-quality, production-ready software and operate systems at massive scale. • Collaborate with cross-functional teams to continuously improve platform effectiveness and operational excellence. • Mentor and support other developers while fostering a culture of ownership, quality, and continuous improvement.

• Analyze business requirements to identify SharePoint-based solutions • Design and develop SharePoint solutions including custom workflows and applications • Collaborate with business stakeholders to ensure solutions meet their needs • Develop SharePoint solutions using development frameworks like .NET and PowerShell • Test and debug SharePoint solutions to ensure quality standards • Provide ongoing maintenance and support for SharePoint solutions • Create and maintain technical documentation for SharePoint solutions

• Implement the specialized pharmacovigilance agents: write system prompts, configure model parameters, build tool-use definitions, and define agent boundaries to ensure precise adverse event processing • Build and iterate prompt chains for each processing step: source document parsing, field extraction, MedDRA coding suggestions, causality assessment logic, narrative drafting, and E2B(R3) output generation • Develop the deterministic rule engine layer: implement ICH E2B field validation checks, MedDRA hierarchy verification, and regulatory logic constraints that operate alongside LLM outputs • Create and maintain evaluation datasets in collaboration with the pharmacovigilance domain team: annotated ground-truth cases, edge case libraries, and regression test suites • Develop and maintain Model Context Protocol (MCP) servers to expose enterprise applications, APIs, databases, and services as standardized tools for AI agents • Implement secure MCP integrations, tool definitions, authentication, and testing to enable reliable agent interaction with internal and external systems • Run accuracy benchmarks, analyze failure modes, and iterate on prompts and agent configurations to improve performance against defined thresholds • Implement the quality control agent's cross-verification logic: configure separate Claude instances, build comparison algorithms, and calibrate confidence scoring • Build human-in-the-loop feedback mechanisms: reviewer interfaces for accept/modify/reject decisions, structured feedback capture, and feedback-to-prompt-improvement pipelines