Job Requirements
Frederick, MD
Confidential Polygraph Unspecified
Career Level not specified
$200,000 - $240,000
Job Description
Piper Companies is looking for a Director of AI to join a thriving insurance company. This is a hybrid position and requires the candidate to be onsite 3 days a week.
Essential Duties of theDirector of AI:
Qualifications of theDirector of AI:
Compensation for theDirector of AI:
This job opens for applications on 3/23/26. Applications for this job will be accepted for at least 30 days from the posting date
#LI-HYBRID
#LI-BM2
Azure Machine Learning, Azure AI Studio, Azure OpenAI Service, Azure Cognitive Services, Azure AI Content Safety, Azure AI Search, Azure AI Speech, Azure AI Vision, Azure AI Language, Azure AI Responsible AI Dashboard, Azure Machine Learning Pipelines, Azure ML Compute Clusters, Azure ML Endpoints, Azure ML MLOps, Azure ML Model Registry, Azure ML Data Labeling, Azure ML AutoML, Azure ML Prompt Flow, Azure Container Instances, Azure Kubernetes Service, AKS Node Pools, AKS Inference Deployment, Azure Functions, Durable Functions, Azure Event Grid, Azure Event Hubs, Azure Service Bus, Azure API Management, Azure Data Lake Storage, Azure Data Lakehouse, Azure Databricks, Delta Lake, Azure Synapse Analytics, Synapse Pipelines, Azure Cosmos DB, Cosmos DB Vector Search, Azure Blob Storage, Azure Virtual Networks, Private Endpoints, VNet Integration, Azure Key Vault, Azure Monitor, Azure Application Insights, Azure Log Analytics, Azure Policy, Azure RBAC, Azure Governance, Azure Landing Zones, Azure DevOps, GitHub Actions, GitHub Advanced Security, Infrastructure as Code, Bicep, Terraform, Cloud-native microservices, Distributed systems design, Container orchestration, Service mesh, Dapr, OpenTelemetry, CI/CD pipelines, AI governance, Model lifecycle management, Model interpretability, Model fairness assessment, Responsible AI compliance, Data governance, Feature engineering at scale, Vector embeddings, Retrieval-augmented generation, RAG architectures, Hybrid search, Knowledge grounding, Enterprise LLM deployment, Model fine-tuning, Prompt engineering, Evaluation datasets, AI safety reviews, Scalable inference, GPU/AI accelerator resource planning, Elastic compute scaling, High-availability AI workloads, Zero-trust architecture, Enterprise identity integration, Entra ID, Tenant-wide AI policy, Data residency requirements, Compliance frameworks, SOC 2 alignment, HIPAA safeguards, ML observability, Data drift detection, Model drift detection, A/B testing for models, Shadow deployments, Blue-green deployments, AI cost optimization, Reserved capacity planning, AI workload cost modeling, Cloud FinOps, Model performance optimization, LLM latency reduction, AI-powered application modernization, AI-driven product strategy, AI portfolio management, Cross-functional AI enablement, AI center of excellence, AI roadmap creation, Enterprise data architecture alignment, Data mesh, Semantic indexing, Meta-prompt management, AI risk management, AI regulatory readiness, Model reproducibility, Data lineage, Telemetry instrumentation, Enterprise-wide AI adoption strategy, AI capability maturity frameworks, Cloud security posture management, Azure Defender for Cloud, AI solution architecture, Generative AI integration patterns, API-first design, Model caching strategies, Low-latency API endpoints, Edge AI deployment, Azure IoT Edge, Real-time inference, Batch inference, Prompt orchestration, Knowledge-base construction, AI-powered automation, Cognitive workload scaling, Pipeline orchestration, Airflow-like orchestration with Azure Data Factory, Enterprise semantic layer, Data cataloging with Purview, Purview Classification, Purview Lineage, Secure ML workspaces, Governed AI experimentation, LLM ops, Enterprise prompt governance, Generative AI product lifecycle, Quality-of-service monitoring for AI apps, AI reliability engineering, Cloud-native resiliency, Distributed tracing, Model-as-a-service architecture, AI workload isolation, Cross-region redundancy, Disaster recovery for AI workloads, Confidential computing for AI, Secure enclave inference, Regulatory model documentation, AI auditability, Enterprise AI communications, Tech leadership alignment, Strategic vendor management, AI-driven innovation pipeline, AI ethics committee collaboration
Essential Duties of theDirector of AI:
- Define and execute the enterprise AI strategy aligned to business objectives and digital transformation priorities
- Evaluate and integrate leading AI capabilities from Microsoft, OpenAI, Anthropic, and Google to ensure the organization leverages best-in-class innovation
- Lead evaluation and decision-making for build vs. buy AI solutions, balancing speed-to-market, total cost of ownership, scalability, security, and strategic differentiation
- Lead the design, development, and deployment of AI and generative AI solutions using Azure AI Services, Azure OpenAI, and other approved AI platforms
- Define and track KPIs to measure AI value realization
- Establish best practices for experimentation, architecture design, vendor evaluation, and AI solution engineering
Qualifications of theDirector of AI:
- Bachelor's degree preferred but not required
- 15+ years experience leading technology, software development, and process improvements
- 5+ years working with Microsoft cloud technologies and within the Azure ecosystem
- 3+ years working and implementing AI solutions
- Experience leading teams through digital transformation and scaling AI capabilities
- Experience with Lifepro/Btrieve desirable
Compensation for theDirector of AI:
- $200,000 - $240,000 (based on experience)
- Comprehensive benefit package; Cigna Medical, Cigna Dental, Vision, 401k w/ ADP, PTO, paid holidays, sick Leave as required by law
This job opens for applications on 3/23/26. Applications for this job will be accepted for at least 30 days from the posting date
#LI-HYBRID
#LI-BM2
Azure Machine Learning, Azure AI Studio, Azure OpenAI Service, Azure Cognitive Services, Azure AI Content Safety, Azure AI Search, Azure AI Speech, Azure AI Vision, Azure AI Language, Azure AI Responsible AI Dashboard, Azure Machine Learning Pipelines, Azure ML Compute Clusters, Azure ML Endpoints, Azure ML MLOps, Azure ML Model Registry, Azure ML Data Labeling, Azure ML AutoML, Azure ML Prompt Flow, Azure Container Instances, Azure Kubernetes Service, AKS Node Pools, AKS Inference Deployment, Azure Functions, Durable Functions, Azure Event Grid, Azure Event Hubs, Azure Service Bus, Azure API Management, Azure Data Lake Storage, Azure Data Lakehouse, Azure Databricks, Delta Lake, Azure Synapse Analytics, Synapse Pipelines, Azure Cosmos DB, Cosmos DB Vector Search, Azure Blob Storage, Azure Virtual Networks, Private Endpoints, VNet Integration, Azure Key Vault, Azure Monitor, Azure Application Insights, Azure Log Analytics, Azure Policy, Azure RBAC, Azure Governance, Azure Landing Zones, Azure DevOps, GitHub Actions, GitHub Advanced Security, Infrastructure as Code, Bicep, Terraform, Cloud-native microservices, Distributed systems design, Container orchestration, Service mesh, Dapr, OpenTelemetry, CI/CD pipelines, AI governance, Model lifecycle management, Model interpretability, Model fairness assessment, Responsible AI compliance, Data governance, Feature engineering at scale, Vector embeddings, Retrieval-augmented generation, RAG architectures, Hybrid search, Knowledge grounding, Enterprise LLM deployment, Model fine-tuning, Prompt engineering, Evaluation datasets, AI safety reviews, Scalable inference, GPU/AI accelerator resource planning, Elastic compute scaling, High-availability AI workloads, Zero-trust architecture, Enterprise identity integration, Entra ID, Tenant-wide AI policy, Data residency requirements, Compliance frameworks, SOC 2 alignment, HIPAA safeguards, ML observability, Data drift detection, Model drift detection, A/B testing for models, Shadow deployments, Blue-green deployments, AI cost optimization, Reserved capacity planning, AI workload cost modeling, Cloud FinOps, Model performance optimization, LLM latency reduction, AI-powered application modernization, AI-driven product strategy, AI portfolio management, Cross-functional AI enablement, AI center of excellence, AI roadmap creation, Enterprise data architecture alignment, Data mesh, Semantic indexing, Meta-prompt management, AI risk management, AI regulatory readiness, Model reproducibility, Data lineage, Telemetry instrumentation, Enterprise-wide AI adoption strategy, AI capability maturity frameworks, Cloud security posture management, Azure Defender for Cloud, AI solution architecture, Generative AI integration patterns, API-first design, Model caching strategies, Low-latency API endpoints, Edge AI deployment, Azure IoT Edge, Real-time inference, Batch inference, Prompt orchestration, Knowledge-base construction, AI-powered automation, Cognitive workload scaling, Pipeline orchestration, Airflow-like orchestration with Azure Data Factory, Enterprise semantic layer, Data cataloging with Purview, Purview Classification, Purview Lineage, Secure ML workspaces, Governed AI experimentation, LLM ops, Enterprise prompt governance, Generative AI product lifecycle, Quality-of-service monitoring for AI apps, AI reliability engineering, Cloud-native resiliency, Distributed tracing, Model-as-a-service architecture, AI workload isolation, Cross-region redundancy, Disaster recovery for AI workloads, Confidential computing for AI, Secure enclave inference, Regulatory model documentation, AI auditability, Enterprise AI communications, Tech leadership alignment, Strategic vendor management, AI-driven innovation pipeline, AI ethics committee collaboration
group id: 10430981
