• Design, build, and operate the corporate AI Gateway using Azure API Management (APIM). • Develop advanced governance, authentication, routing, and observability policies for generative AI workloads. • Integrate multiple AI providers, including Azure OpenAI, Azure AI Foundry, GCP Vertex AI, AWS Bedrock, and Adobe Firefly. • Implement FinOps mechanisms to control consumption, per-subscription quotas, token budgeting, and cost attribution. • Develop and maintain infrastructure as code (IaC) using Terraform/OpenTofu. • Build and evolve CI/CD pipelines with GitHub Actions using OIDC authentication. • Create centralized observability mechanisms using Application Insights, KQL, Azure Workbooks, Datadog, and CloudWatch. • Develop APIM policies for SSE streaming, request/response transformation, retry, fallback, and backend routing. • Work on platform security using Azure AD, JWT validation, WAF tuning, Front Door, and Key Vault. • Automate operations and administrative workflows using Bash, PowerShell, and Python. • Produce technical documentation, OpenAPI specs, and materials for technical and non-technical stakeholders. • Support internal teams in the secure and scalable adoption of generative AI. • Translate technical limitations and architectural decisions into clear recommendations for different audiences. • Work autonomously to identify needs, prioritize improvements, and continuously evolve the platform.

• Design and own environment-management foundations (env repos, env modules, promotion patterns) for consistent Dev/Stage/Prod provisioning. • Publish and govern Terraform modules, managing registry operations, versioning, reviews, documentation standards, and deprecation. • Administer Terraform Cloud workspaces, policies/approvals, secure variables, and run workflows for scalable delivery. • Define and enforce platform IAM standards across Azure and tooling (RBAC, managed identities, service principals), including SP→MI modernization and governance integration. • Implement secrets-binding patterns with Azure Key Vault, including access models, references, and rotation across IaC workflows. • Lead FinOps enablement: tagging/labeling, budgets/alerts, and cost/productivity KPI dashboards using Cloudability. • Administer platform tooling (GitHub, Azure DevOps, Docker Hub licensing) including governance, cost/resource optimization, and support. • Integrate platform workflows with enterprise systems such as ServiceNow CMDB and IdentityNow for auditable provisioning and access governance. • Build and operationalize VM fleet patterns (image strategy, patching/maintenance, scaling, reliability) with automation and runbooks. • Establish infrastructure guardrails (CI validation/testing, policy checks, drift detection) and drive adoption via templates and infra-testing standards. • Provide certificate lifecycle automation and CA integrations (issuance, renewal, rotation) for platform components. • Produce and maintain platform documentation, templates, release notes, and onboarding materials; run office hours and support motions to drive self-service adoption. • Drive platform feedback loops, community building, and evangelism; translate signals (NPS/surveys) into roadmap inputs. • Administer Copilot/agent enablement and MCP server catalog integrations with instructions/runbooks for safe, consistent usage.

• Own the CI/CD platform for application delivery: design reusable pipeline templates, secure defaults, quality gates, and developer enablement. • Build and operate infrastructure pipelines for environment lifecycle management, including provisioning, promotion, drift detection, and controlled rollouts. • Establish and maintain delivery governance with policy catalogs, change control, exception workflows, traceability, and audit evidence. • Implement automated compliance and security checks in pipelines using policy-as-code and scanning, and continuously improve signal quality. • Enable safe releases at scale through zero-downtime deployment patterns, progressive delivery, feature flag integration, and standardized rollback and verification practices. • Automate change management integrations with ServiceNow and CAB workflows to reduce manual overhead while preserving controls and traceability. • Operate and evolve build infrastructure (runners/agents and build pools): manage performance, reliability, hardening, patching, and cost awareness. • Implement software supply chain protections including artifact provenance/attestation, signing/verification, and private artifact repository patterns. • Integrate DevSecOps tooling into delivery flows and enable teams to remediate findings through reporting, triage, and automation. • Build internal automation (apps/bots) that simplify developer workflows and enforce standards with minimal friction. • Provide standardized automated testing capabilities (acceptance, performance, resiliency/chaos, operational acceptance tests) as pipeline stages and quality gates. • Enable observability onboarding and standards with instrumentation guidance, dashboards-as-code, and alerting in partnership with solution teams. • Partner with SRE and operations to improve reliability practices, availability policies, incident response, and on-call readiness.

• Own the connector and service integration layer that powers AI workflows across the company. • Design and ship execution environments for agents and higher-autonomy AI workflows, including isolation boundaries and access controls. • Build reusable platform services, golden paths, and self-service templates that reduce setup friction for teams building on AI. • Productize onboarding so it works reliably for both developers and non-developers without depending on manual intervention or tribal knowledge. • Define and enforce technical standards for agent execution, evaluation loops, and deployment. • Partner with Security and IT to ship deployable patterns for higher-risk AI capabilities. • Own the AI governance layer: access controls, audit trails, approval criteria, and deployment boundaries for agentic workflows. • Set the reliability, observability, and operational bar for AI-specific infrastructure. • Act as the technical escalation point when onboarding or platform issues block rollout. • Reduce the company's dependence on individual heroics by turning exception handling into repeatable paths.