Elevance Health
AI Chief Engineer
Chief AI Engineer is responsible for enterprise data architecture for the Artificial Intelligence (AI) function. How You Will Make An Impact: Strategic initiatives of data analysis, interpreting results and developing actionable insights and recommendations across the enterprise. Responsible for the design, development, deployment, maintenance, enhancement and support of AI Engineering function. Influence in Matrixed Orgs: Cross functional guilds (Security, Legal, Risk, Compliance, Data, LOBs); enablement programs, community of practice, and outcome driven collaboration. Launch an enterprise AI marketplace & registry with model cards, lineage, data controls, and automated approvals across security, legal, and procurement. Implement an AI gateway for role/entitlement aware access, metering/chargeback, and centralized policy enforcement across cloud runtimes. Stand Up horizontal AI services (prompt/RAG APIs, vector & feature stores, guardrails/safety, key management, evals/benchmarks) with SDKs and paved road templates. Deliver observability & risk with evaluation pipelines, red team/safety tests, drift/quality monitoring, and explainability and post deploy QA. Embed privacy, security, and compliance via zero trust patterns, private networking, KMS/Vault, policy based data minimization, and auditable traceability. Scale AI workloads on Kubernetes with autoscaling, canaries, and multi region failover; standardized golden paths to accelerate adoption across BUs. Drive platform integrations with line of business and back office systems via event streams and standardized connectors. Build governance playbooks and chaired architecture reviews; aligned architecture decisions with risk appetite, SLOs, and cost/performance objectives. Minimum Requirements: Requires a Bachelor's degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 12 or more years' of experience; or any combination of education and experience in configuration management, which would provide an equivalent background. Preferred Skills, Capabilities & Experiences: Enterprise AI Marketplace/Registry: Governed self service publishing & consumption of models, AI services, prompts, datasets, and agents with lifecycle metadata, approvals, and chargeback. Horizontal AI Services at Scale: Shared, SLA backed servicesprompt/RAG, vector & feature stores, guardrails/safety, evals, observability, key managementdelivered as APIs/SDKs enterprise wide. E2E AI Platform Strategy & Integration: Roadmaps spanning AI experimentation, development, data mesh, MLOps, observability, and an AI gateway for policy & traffic; integrated with SDLC policies and procedures. AI Architecture: Blueprints for AI solutions, agents, tool use/function calling, retrieval, safety pipelines, multi tenant isolation, and zero trust access; multi region resiliency and performance engineering. Cloud Engineering: Multi cloud and open source with Terraform as Code, GitOps, Kubernetes, and SRE practices (SLOs/error budgets). Traditional AI ? GenAI ? Agents: Production ML (forecasting, NLP, fraud/risk), LLM/RAG pipelines, fine tuning/LoRA, synthetic data, evaluation harnesses, policy driven guardrails, agent orchestration.
Chief AI Engineer is responsible for enterprise data architecture for the Artificial Intelligence (AI) function. How You Will Make An Impact: Strategic initiatives of data analysis, interpreting results and developing actionable insights and recommendations across the enterprise. Responsible for the design, development, deployment, maintenance, enhancement and support of AI Engineering function. Influence in Matrixed Orgs: Cross functional guilds (Security, Legal, Risk, Compliance, Data, LOBs); enablement programs, community of practice, and outcome driven collaboration. Launch an enterprise AI marketplace & registry with model cards, lineage, data controls, and automated approvals across security, legal, and procurement. Implement an AI gateway for role/entitlement aware access, metering/chargeback, and centralized policy enforcement across cloud runtimes. Stand Up horizontal AI services (prompt/RAG APIs, vector & feature stores, guardrails/safety, key management, evals/benchmarks) with SDKs and paved road templates. Deliver observability & risk with evaluation pipelines, red team/safety tests, drift/quality monitoring, and explainability and post deploy QA. Embed privacy, security, and compliance via zero trust patterns, private networking, KMS/Vault, policy based data minimization, and auditable traceability. Scale AI workloads on Kubernetes with autoscaling, canaries, and multi region failover; standardized golden paths to accelerate adoption across BUs. Drive platform integrations with line of business and back office systems via event streams and standardized connectors. Build governance playbooks and chaired architecture reviews; aligned architecture decisions with risk appetite, SLOs, and cost/performance objectives. Minimum Requirements: Requires a Bachelor's degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 12 or more years' of experience; or any combination of education and experience in configuration management, which would provide an equivalent background. Preferred Skills, Capabilities & Experiences: Enterprise AI Marketplace/Registry: Governed self service publishing & consumption of models, AI services, prompts, datasets, and agents with lifecycle metadata, approvals, and chargeback. Horizontal AI Services at Scale: Shared, SLA backed servicesprompt/RAG, vector & feature stores, guardrails/safety, evals, observability, key managementdelivered as APIs/SDKs enterprise wide. E2E AI Platform Strategy & Integration: Roadmaps spanning AI experimentation, development, data mesh, MLOps, observability, and an AI gateway for policy & traffic; integrated with SDLC policies and procedures. AI Architecture: Blueprints for AI solutions, agents, tool use/function calling, retrieval, safety pipelines, multi tenant isolation, and zero trust access; multi region resiliency and performance engineering. Cloud Engineering: Multi cloud and open source with Terraform as Code, GitOps, Kubernetes, and SRE practices (SLOs/error budgets). Traditional AI ? GenAI ? Agents: Production ML (forecasting, NLP, fraud/risk), LLM/RAG pipelines, fine tuning/LoRA, synthetic data, evaluation harnesses, policy driven guardrails, agent orchestration.