• Be the domain credibility of the pod • Embed with partner teams to identify AI solutions • Lead discovery and requirements • Conduct hands-on demos tailored to workflows • Drive change management for successful deployments • Regular on-site travel expected, potentially 50%+ of time

Role Description The emphasis of this role is to be the dedicated technical lead for developing WWT’s defensive AI operations, specifically focused on utilizing massive open-source models to solve the vulnerability management problem at an enterprise scale. This position requires an elite technical leader who is adept at advanced machine learning, can speak to complex exploit mechanics, and ensure our defensive posture rapidly evolves to keep pace with AI-accelerated offense and frontier-model-class intelligence. This role will also involve guiding the organization away from manual triage by anticipating exploit chaining and integrating the largest open-source AI models to ensure our defensive strategies remain ahead of advanced adversaries. Responsibilities - Partner with the GS&A Cyber Practices to utilize models and evaluate attack paths and how AI can help defenders operate at the speed of attackers. - Deliver measurable outcomes to customers and partners as to how AI and ML-based science improves cybersecurity defenses. - Participate in industry forums on the topic of using AI for defense and perform technical demonstrations on how AI is used for offensive operations. - Architect and deploy advanced machine learning models that analyze, score, and prioritize vulnerabilities at an unprecedented scale. - Continuously evaluate, integrate, and fine-tune frontier models to match and counter frontier-model-class attack methodologies and AI-driven exploit chaining. - Shift organizational operations entirely away from manual triage by providing automated, model-assisted workflows for immediate remediation guidance. - Ingest and model complex threat data, scanning code with open-source models to train systems on handling the exploding volume of automated vulnerability reports. - Dedicate 100% of operational bandwidth to defensive cybersecurity measures, using AI to neutralize emerging threats before they can be weaponized against our customers. - Act as the technical expert for data architecture, statistical modeling, and advanced machine learning applied to massive enterprise datasets. - Drive the vulnerability prioritization problem space autonomously from concept to customer-facing deployment without distraction. - Apply deep cybersecurity domain knowledge, specifically regarding vulnerability management, exploit mechanics, and patch gap mitigation, to all AI models. Qualifications - Master’s degree or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Cybersecurity, or a related highly technical discipline. - 8+ years of progressive experience in data science, machine learning, or AI engineering, with a minimum of 3 years directly embedded in cybersecurity, threat intelligence, or vulnerability management. - Deep, hands-on expertise with the Hugging Face ecosystem (Transformers, Accelerate, PEFT). - Proven track record of deploying, quantizing, and fine-tuning (e.g., LoRA, QLoRA) large open-source models (Llama, Mistral, Qwen) for production environments. - Comprehensive understanding of the vulnerability lifecycle, patch gap analysis, exploit chaining, and standard industry frameworks (MITRE ATT&CK, CISA KEV, EPSS, CVSS). - Expert-level coding proficiency in Python and leading deep learning frameworks, specifically PyTorch. - Extensive experience operating in high-performance computing environments. - Adept at managing GPU workloads, CUDA optimization, and deploying models on distributed infrastructure (e.g., Kubernetes, Ray, vLLM). - Strong capability in building high-throughput data pipelines to ingest, clean, and process massive datasets of threat intelligence, code repositories, and telemetry. - Demonstrated ability to own a complex problem space entirely, translating theoretical data science concepts into highly scalable, real-world defensive tools at an accelerated pace. Requirements - A reasonable estimate of the current base pay range for this position is $240,000.00 to $260,000.00 annually. - Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. - The range for this position in other geographic locations may differ. - Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay. Benefits - Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program. - Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement. - Paid Time Off: PTO and Sick Leave (starting at 20 days per year) & Holidays (10 per year), Parental Leave, Military Leave, Bereavement. - Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program.

Title: Senior Agentic AI Specalist Midwest Family Mutual Insurance Company   Urbandale, Iowa, United States   Information Technology Job Description:  Position Summary The MFM IT team has a long history of delivering and maintaining core business systems in a mid-sized Property & Casualty insurance environment. As we expand our development team and adopt new AI development tools, we are making a deliberate investment in building this capability correctly from the start. As we begin to build agentic AI capabilities within our enterprise system, we are looking for an experienced engineer with strong knowledge of agentic AI systems to help guide this effort. The Senior Agentic AI Specialist will work closely with MFM's Principal Architect, who leads overall platform architecture, and partner with senior developers to design, implement, and improve agent-based solutions. This role will bring specialized expertise in areas where we are still developing depth, including agent orchestration, retrieval pipelines, LLM evaluation, and prompt design. This is an ongoing platform effort, not a one-time project. The work will evolve over time as we expand capabilities, integrate new tools, and improve system quality. Our core systems are built on a Microsoft/.NET stack. As we develop our agentic AI capabilities, we expect to leverage cloud-based tooling (likely within Azure) where appropriate. We are pragmatic about using the right tools for the problem, while maintaining alignment with our existing platform and operational requirements. At Midwest Family we value practical experience, clear thinking, and the ability to apply these skills in a real-world environment. Application Requirements   Cover Letter and Resume:  Please include the following in your cover letter - A description of an AI or agent-based system you have worked on, including your role and how you evaluated quality   - Your approach to reducing incorrect or unsupported outputs   - An example of how you supported other developers or teams in adopting AI capabilities   - Your physical remote work location state - Salary expectations  Responsibilities: - Platform Architecture & Evolution: Provide input into AI-related architecture decisions. Evaluate tools, models, and approaches, and make practical recommendations based on trade-offs and outcomes. - Agent Lifecycle Management: Design, build, and maintain agents. Define prompts and configurations, support routing decisions, and monitor performance in production. - RAG Pipeline Engineering: Improve retrieval quality by working with chunking strategies, embeddings, search methods, and metadata filtering. Support onboarding of new knowledge sources.  - Quality & Hallucination Reduction: Help reduce incorrect or unsupported outputs through system design, testing, and monitoring. Apply practical approaches across prompts, retrieval, and model behavior. - Evaluation & Testing: Develop and maintain evaluation approaches to measure output quality, including relevance and accuracy. Support regression testing and identify failure patterns. - Prompt Engineering: Create and maintain prompts for different agents. Diagnose unexpected behavior and develop reusable patterns where appropriate. - Tool Integration: Support integration with internal systems and APIs. Help manage tool access and ensure reliable execution and error handling. - Observability & Operations: Contribute to logging, tracing, and monitoring. Support analysis of system behavior, cost, and performance in production. - Governance & Configuration: Work with configuration across agents, prompts, and models. Ensure changes are version-controlled and reviewed. - Developer Support: Work with other developers to share knowledge, review designs, and establish consistent approaches. - Research & Evaluation: Stay current with new models and tools. Evaluate them in a structured way and recommend adoption where appropriate. Requirements: Education & Certification - College degree or Programming Certification (preferred) - 5+ years of equivalent work experience Required Experience - Experience building or working with AI/LLM-based systems in a production or near-production environment  - Strong understanding of agent-based patterns (ReAct, tool usage, etc.)  - Experience with retrieval-based systems (RAG), vector stores, or search pipelines  - Familiarity with evaluating non-deterministic system outputs  - Experience improving system quality through testing and iteration  - Strong software engineering skills required. Experience with Python, TypeScript, or C# preferred  - Ability to work across technologies and integrate AI solutions into an existing enterprise environment  Preferred Experience - Experience working with multiple LLM providers  - Familiarity with emerging tools and frameworks in the agentic AI space  - Experience integrating with enterprise systems and data sources  - Experience mentoring or supporting other developers  Personal Attributes - Strategic thinker with strong analytical and problem-solving skills - Technically proficient with broad systems knowledge - Excellent interpersonal, written, and verbal communication skills - Self-directed, collaborative, and highly organized - Customer-oriented and able to navigate high-pressure environments - Flexible and adaptable in regards to learning and understanding new technologies. - Exceptional organizational skills and ability to keep track of multiple projects at a time. - Ability to conduct research into software-related issues and products. - Technically proficient. - Exceptional attention to detail. - Ability to work both independently and in a team-oriented, collaborative environment. Work Conditions - Remote work environment following core hours 8AM-5PM Central Time - Overtime may be required to meet project deadlines or monitor system processes. - Sitting for extended periods of time. - Dexterity of hands and fingers to operate a computer keyboard, mouse, and other devices. - Minimal travel will be required for the purpose of off-site software training and team meetings. - Physically able to participate in virtual and in-person training sessions, presentations, and meetings.

• Perform and manage audit procedures directly, including planning, execution, and documentation. • Review and validate AI-generated audit procedures, workpapers, and documentation. • Identify gaps, errors, inconsistencies, and edge cases in AI-generated audit outputs. • Develop quality review frameworks, checklists, and methodologies for AI-assisted audits. • Collaborate closely with engineering and product teams to improve AI accuracy, reliability, and audit-awareness. • Translate hands-on audit experience into concrete feedback that shapes product decisions and AI workflows. • Stay up to date with professional standards (GAAS, PCAOB, IAASB) and ensure all outputs comply. • Document best practices and contribute to building internal knowledge bases. • Establish continuous feedback loops that improve AI performance over time. • Engage directly with clients on audit methodology questions and help interpret AI-generated content. • Manage client relationships and deadlines, ensuring audit deliverables are completed accurately and on time — coordinating across internal and client teams to keep engagements on track. • Jump into whatever is needed — client calls, QA, research, workpaper review — to ensure smooth execution.