Sr. AI Platform Engineer- Contract - Reston, VA

The AI Platform Engineer translates AI reference architectures into secure, scalable cloud configurations that enable development teams to build and operate AI solutions in a regulated healthcare environment. Reporting into the AI Platform team and working closely with the Lead AI Platforms (Domain Architect), this role operationalizes platform standards, automates guardrails, and ensures every AI workload deployed meets enterprise security, compliance, and governance requirements.
Key Responsibilities

(1)Platform Engineering & Cloud Configuration
-- Convert approved AI reference architectures into deployable cloud configurations across Azure, MuleSoft, and Salesforce Agentforce, using infrastructure-as-code (Terraform, Bicep, ARM) and CI/CD pipelines.
-- Build and maintain reusable platform components, modules, and landing zones that accelerate AI solution delivery across product teams.
-- Configure and operate core AI platform services (e.g., Azure OpenAI, Azure AI Foundry, AI Search, MuleSoft integration layers, Salesforce Agentforce agents, Antrhopic, OpenAI) in alignment with enterprise architecture standards.
-- Implement environment promotion patterns (dev --€-'''''''' test --€-'''''''' prod), secrets management, and observability tooling for AI workloads.
(2) Governance & Guardrails Enablement
-- Translate AI governance policies (risk tiering, model approval, PHI/PII handling, audit logging) into enforceable technical controls: policy-as-code, Azure Policy, RBAC, network isolation, and data egress restrictions.
-- Implement controls that enforce HIPAA, CMS, and internal compliance requirements for AI solutions, including Zero Data Retention configurations, audit log integration, and prompt/response logging where required.
-- Partner with the AI Governance Lead and Coordinator to ensure platform configurations match documented governance posture and are audit-ready.
-- Configure model gateways, content safety filters, bias/PII safeguards, and usage telemetry to support responsible AI operations at scale.
(3) Developer Enablement
-- Deliver paved-path templates, starter kits, and self-service capabilities that allow product and engineering teams to build AI solutions safely without recreating platform components.
-- Provide technical support, documentation, and office hours to development teams consuming the AI platform.
-- Collaborate with Consultant Product Owners and platform architects to translate solution requirements into platform capabilities and backlog items.
(4) Operations & Reliability
-- Monitor platform health, capacity, cost, and consumption; implement automation to optimize spend and performance.
-- Support incident response, root-cause analysis, and continuous improvement of platform reliability and security posture.
-- Maintain platform documentation, runbooks, and architectural decision records.

Required Qualifications
- Bachelor''''''''s degree in Computer Science, Engineering, or related field; equivalent experience considered.
- 5+ years of cloud platform engineering experience, with 2+ years focused on Azure (or comparable hyperscaler).
- Hands-on experience with infrastructure-as-code (Terraform/Bicep), CI/CD pipelines (Azure DevOps, GitHub Actions), and policy-as-code frameworks.
- Working knowledge of AI/ML platform services Azure OpenAI, AI Foundry, vector databases, model gateways, or equivalent.
- Experience implementing technical controls for regulated data environments (HIPAA, PCI, or similar).
- Strong understanding of identity, networking, encryption, and secrets management patterns in the cloud.

Preferred Qualifications
- Platform familiarity across Azure, MuleSoft, and Salesforce Agentforce ability to configure, integrate, and govern AI workloads spanning all three platforms.
- Experience operating AI or data platforms in a healthcare payer or other regulated industry.
- Familiarity with AI governance frameworks (NIST AI RMF, ISO 42001) and translating policy into technical enforcement.
- Experience with document intelligence, RAG architectures, or agentic AI patterns.
- Azure certifications (AZ-305, AZ-400, AI-102), MuleSoft Certified Developer/Architect, or Salesforce Agentforce/Platform credentials a plus.