• Participate in active client AI projects as the hands-on AI specialist • Design and build AI agent workflows, prompt chains, and orchestration logic • Develop and refine prompts for specific business use cases (prompt engineering) • Analyze existing client workflows and identify opportunities for AI enhancement • Create working prototypes and proofs-of-concept to validate approaches • Collaborate with developers on implementation handoff and technical specifications • Support presales engagements with AI solution proposals and feasibility assessments • Conduct business domain analysis to identify where AI creates genuine value • Translate client business problems into AI-addressable solutions • Prepare demonstrations and prototype artifacts for client presentations • Provide input on effort estimation for AI components of proposals • Monitor AI news, model releases, tool updates, and emerging techniques • Evaluate new AI platforms, frameworks, and services for potential adoption • Experiment with new approaches and share findings • Document patterns, techniques, and lessons learned from project work • Contribute to internal knowledge bases and training materials • Design agentic workflows using orchestration frameworks (e.g., n8n, LangGraph, custom pipelines) • Optimize existing business workflows by introducing AI capabilities • Define conversation flows, tool usage patterns, and handoff logic for AI agents • Specify context management, memory strategies, and retrieval patterns

• Build and maintain trusted relationships with senior executives within P&C insurance organizations including Chief Claims Officers, Chief Data Officers, and underwriting leaders. • Develop deep understanding of client priorities across claims automation, underwriting optimization, fraud detection, risk modeling, catastrophe analytics, and regulatory compliance. • Serve as a strategic advisor guiding clients through AI adoption decisions, from use case scoping through production deployment and model governance. • Lead executive conversations on how AI — and specifically human-in-the-loop approaches — can improve efficiency, accuracy, and defensibility in insurance workflows. • Navigate complex multi-stakeholder environments: actuarial, claims, IT, legal/compliance, and data science functions often have divergent priorities; you’ll need to align them. • Proactively identify expansion opportunities within accounts; map and cultivate economic buyers and champions. • Drive new logo acquisition and account expansion within large enterprise P&C insurance across major US and European markets. • Develop strategic account plans with clear expansion paths from initial use case to enterprise-wide AI program. • Lead commercial engagements end-to-end: discovery, proposal development, commercial structuring, and executive presentation. • Partner with internal teams to shape consulting engagements tailored to insurance operations. • Understand P&C procurement dynamics, including RFP processes, vendor risk management, data governance and residency requirements, and regulatory constraints — and navigate them effectively. • Identify high-impact AI use cases across the P&C value chain, including: claims document processing and intelligent document understanding; aerial and satellite imagery analysis for property assessment; NLP for policy, coverage, and regulatory text; underwriting data enrichment and risk model validation; FNOL automation and subrogation workflows. • Conduct discovery sessions and workshops to identify opportunities for AI-driven transformation across insurance value chains. • Translate client operational and regulatory challenges into compelling AI solutions involving data annotation, machine learning model development, and automation pipelines. • Collaborate with data scientists and engineering teams to ensure successful project execution. • Ensure timely delivery, scope, and client satisfaction throughout project lifecycles. • Stay informed on emerging trends in AI within insurance, including risk modeling, intelligent document processing, and fraud detection. • Contribute to external visibility through participation in industry events and client workshops, positioning the company as a trusted AI consulting partner. • Demonstrate expertise in the regulatory landscape shaping AI in insurance: state DOI guidelines, NAIC model bulletins, EU AI Act implications for European carriers, and model risk management frameworks. • Lead cross-functional teams of consultants, data scientists, and engineers. • Foster collaboration and innovation across delivery teams. • Support knowledge sharing and best practices in AI consulting engagements.

• Define and run SnowHeap’s AI governance program: policies, control library, risk register, exception handling, and sign-offs (from ideation to production). • Map laws and frameworks (EU AI Act, GDPR/PDPL/DIFC DPL, NIST AI RMF, ISO/IEC 42001 & 27001, SOC 2) to concrete technical controls in our products and client projects. • Build an evaluation harness for LLMs/agents: golden sets, scenario tests, adversarial probes, offline evals, and online A/Bs; track hallucination, safety, bias, privacy leakage, robustness, cost, and latency. • Implement guardrails (PII detection, jailbreak/prompt-injection defenses, output filters, content safety) and wire them into pipelines (LangChain/LangGraph, CrewAI/Agno). • Stand up audit-ready telemetry: data lineage, prompt/response logging with redaction, model cards, decision traces, and approval workflows (LangSmith/observability tools). • Partner with Security/Privacy on DPIAs/TRA, retention, DLP, key management, access controls, and vendor risk (OpenAI/Anthropic terms, Azure/GCP/AWS). • Lead red-teaming exercises; coordinate incident response playbooks for model failures and safety regressions. • Review prompts, fine-tunes, and datasets for policy compliance; curate evaluation datasets and “go/no-go” acceptance criteria. • Coach engineers, sales, and clients; write crisp docs and checklists; run internal trainings and readiness reviews. • Contribute to proposals and client audits; turn compliance into a product advantage.

• Production reliability: ensuring HAP (our Hybrid AI Platform) and customer deployments operate dependably under real traffic, real data, and real regulatory constraints • Agent trust: building the frameworks — technical and operational — that allow enterprise customers to trust autonomous AI agents doing work on their behalf • A production quality operating system: quality gates, phase transition criteria, incident taxonomy, observability spec across our 6-layer Reference Architecture • A continuous validation framework for agentic workflows — autonomous evaluation pipelines that catch regression without human intervention • An agent decision qualification framework: risk-tiered oversight for autonomous agent decisions • A trust evidence system: the observable signals that enterprise customers use to extend trust to agents operating on their behalf • Production observability: instrumentation across Ingest, Prepare, Serve, Orchestrate, Monitor, and Optimize layers of the Reference Architecture • A post-mortem and CAPA system: every production incident produces a root cause, a corrective action, and a new test that prevents recurrence