Head of AI Platform Engineering - Execution Plane (AI)

Location: New York, New Jersey, or Pennsylvania; up to 10% travel within the US. Candidates must be legally authorized to work in the United States without current or future employer sponsorship.

Base salary: $152,290.00–$250,195.00 per year, plus potential incentive compensation.

Company

hirify.global is a Fortune 250 mutual insurance and wealth management company providing life, disability, dental, and other benefits products and services.

What you will do

• Lead development and implementation of the enterprise platform execution layer for agentic AI workloads, model gateways, agent runtimes, tool/action gateways, MCP servers, orchestration frameworks, and AI execution engines.
• Own the roadmap and delivery for runtime services, model inference, retrieval, orchestration, tool invocation, workflow execution, knowledge bases, retrieval pipelines, inference services, and deployment patterns.
• Lead and develop software engineering teams, including hiring, coaching, performance management, and senior technical talent development.
• Partner with control plane, developer experience, data engineering, security, architecture, operations, domain application teams, stakeholders, and technology partners.
• Drive reliable, scalable, low-latency execution services with observability, graceful degradation, retries, rate limits, operational resilience, and production support.
• Establish engineering excellence through architecture standards, design reviews, testing, CI/CD, infrastructure automation, incident response, SLOs, runbooks, security, compliance, and auditability.

Requirements

• Bachelor’s or master’s degree in computer science, engineering, management, or a related field.
• Strong people leadership experience managing software engineering teams, including hiring, coaching, performance management, and developing senior technical talent.
• Deep hands-on experience designing, building, and operating production-grade distributed systems, AI/ML platforms, data platforms, or cloud-native runtime services.
• Experience leading teams responsible for platform execution layers, including runtime services, model inference, retrieval, orchestration, tool invocation, workflow execution, or production AI operations.
• Strong understanding of enterprise APIs, data access patterns, retrieval-augmented generation, embeddings, vector/search systems, context engineering, and governed access to systems of record.
• Experience building reliable and scalable execution services with clear contracts, observability, graceful degradation, retry patterns, rate limits, and operational resilience.
• Ability to drive design reviews, architecture standards, testing, CI/CD, infrastructure automation, incident response, SLOs, runbooks, and production support mechanisms.
• Strong understanding of enterprise security and compliance for runtime execution, including identity propagation, auditability, data handling, least-privilege access, secrets management, and environment isolation.
• Strong communication and influence skills, including simplifying complex runtime, data, and AI execution topics for senior stakeholders and aligning teams around reusable platform patterns.

Nice to have

• Experience with agentic AI systems, LLM platforms, model gateways, agent runtimes, tool/action gateways, MCP servers, orchestration frameworks, or AI execution engines.
• Experience with AWS AI execution services and cloud-native patterns, including Bedrock, AgentCore, SageMaker, Lambda, Step Functions, API Gateway, EKS, DynamoDB, S3, IAM, CloudWatch, and related DevOps tooling.
• Familiarity with RAG systems, enterprise search, vector databases, embedding models, rerankers, knowledge bases, context engineering, and governed retrieval from enterprise data sources.
• Experience designing multi-tenant runtime platforms with environment isolation, workload identities, RBAC/ABAC, cross-account access, quotas, throttling, and secure service-to-service communication.
• Background in MLOps, LLMOps, AIOps, model lifecycle management, evaluation pipelines, model serving, inference optimization, or production AI operations.
• Experience with AI workload observability, performance and cost optimization, reusable platform abstractions, SDKs, runtime adapters, reference architectures, deployment templates, and governed enterprise connectors.
• Comfort operating in a regulated, matrixed environment where production safety, auditability, resiliency, and vendor optionality are core design principles.

Culture & Benefits

• Supportive and flexible environment focused on professional and personal goals.
• Skill-building, leadership development, philanthropic opportunities, and collaboration with diverse colleagues.
• Medical, dental, supplemental health, life, vision, and disability coverage with no waiting period.
• 401(k) with match, tuition assistance, paid parental leave, and backup family care.
• Flexible time off, dress code, and work location policies, with modern collaborative work environments.
• Commitment to ethical standards, community volunteering, education alliances, sustainability, and diversity and inclusion.