Applied AI & Platform Engineer
Required skills
5-10 years professional software engineering experience; strong Python (services, APIs, testing), Cloud infrastructure experience (Azure preferred), operating production systems; cloud security fluency (identity, RBAC, secrets management, logging/auditing), CI/CD experience (Git-based pipelines, automated tests, deployment gates, rollback awareness), Strong testing, documentation, and operational discipline
Preferred skills
Data engineering fundamentals (schemas, pipelines, quality checks) within governed environments, Infrastructure as Code (Bicep/Terraform), containerization (Docker), and API management patterns, Agentic patterns (tool use, orchestration, state management) and practical guardrails, Microsoft Graph and/or strong enterprise identity/API permissioning patterns
Technologies
Azure, Python, LLM, APIs, CI/CD, Git, cloud security, observability, Data Lake, containerization, Bicep/Terraform, Docker, Microsoft Graph
Responsibilities
Build end-to-end workflow implementations from requirements to production: APIs, services, orchestration, and integrations, Integrate AI capabilities into products and internal tools (LLM calls, tool/function calling, guardrails, evals, response structuring), Design and implement scoped agent workflows with clear boundaries, tools, permissions, fallbacks, and measurable outcomes, Collaborate with vendors to deliver ROI-focused use-cases: define interfaces, validate delivery quality, and productionize outputs, Establish strong observability: structured logging, tracing, metrics, dashboards, alerting, and runbooks, Partner with data teams on Data Lake access patterns, workflows, metadata, governance, and reliability
Seniority
Mid to senior level
Domain
AI, cloud computing, enterprise software, data engineering, product integration
