Principal Software Engineer (Cloud and AI Platforms)

BlueFlag LLP•Pittsburgh, PA

1h•Remote

About The Position

BlueFlag is seeking a principal-level software engineer to lead the design and delivery of cloud-native applications, services, and data platform capabilities. This opportunity is remote. In this role, you will be a hands-on technical leader who can build production software end-to-end—while also owning the cloud, Kubernetes, and data engineering patterns required to run secure, enterprise-grade platforms. This is not a platform-only role. We need a leader who can matrix across initiatives that are software-engineering-centric (APIs, backend services, full stack web applications), and also lead teams in deploying and operating the underlying cloud/data tooling that those applications depend on (e.g., Databricks, Synapse, Starburst, Immuta, Collibra). You will also help shape how we enable AI-powered applications, including agent-based workflows, on top of governed data and reliable infrastructure. Why Join BlueFlag At BlueFlag, we build mission-driven software and platforms for large, complex environments. You will have meaningful technical ownership and the ability to set direction—while still staying hands-on in the code. If you want to build excellent software, lead engineers, and operate in the real-world constraints of cloud security, data governance, and enterprise scale, this role offers both impact and growth.

Requirements

Principal-level experience building and leading delivery of production software (backend services and/or full stack applications) with strong design and implementation depth.
Demonstrated ability to lead engineering across multiple efforts (mentorship, design leadership, code reviews, technical standards).
Strong proficiency in at least one backend language commonly used for enterprise software (e.g., C#/.NET, Java, Python, Go, or similar) and comfort working across the stack as needed.
Strong understanding of API design, distributed systems fundamentals, performance, and operational reliability.
Strong Kubernetes experience operating production workloads (not just deploying to someone else’s cluster).
Hands-on experience engineering and operating solutions on Azure or AWS (networking, identity, security, and operations).
Experience with CI/CD, automation, and infrastructure-as-code (e.g., Terraform).
Experience with observability (monitoring, logging, tracing), incident response, and root-cause remediation.
Demonstrated interest in AI agents, AI infrastructure, or AI-enabled applications (professional work, personal projects, research, or equivalent).
US Citizen: Must be a citizen of the United States
Security Clearance: Must be able to obtain a public trust clearance. Must be eligible to work in the United States.

Nice To Haves

Experience with enterprise data/platform tools such as Databricks, Synapse, Starburst, Immuta, Collibra
Experience building secure integration patterns across identity, entitlements, and data governance (auditability, policy enforcement).
Experience with lakehouse patterns and data formats (e.g., Delta Lake, Parquet) and production data lifecycle management.
Experience enabling AI systems in production (e.g., retrieval patterns, vector search, orchestration frameworks, evaluation/monitoring, model serving), in regulated environments.
Experience supporting MLOps pipelines

Responsibilities

Architect, build, and maintain production-grade software (services, APIs, and web applications) with strong engineering discipline: code quality, testing, observability, and operational readiness.
Lead design reviews, establish engineering standards, and mentor engineers across multiple projects—especially software-heavy initiatives.
Build integration layers and service patterns that connect applications to data platforms, identity systems, and enterprise tooling.
Drive modern SDLC practices: trunk-based development or equivalent, strong code review culture, automated testing, and predictable release workflows.
Design and operate Kubernetes-based application platforms, including deployment standards, networking/ingress, upgrades, reliability, and day-2 operations.
Implement and mature CI/CD pipelines and release automation for both applications and infrastructure (including GitOps patterns where appropriate).
Build and maintain infrastructure-as-code (e.g., Terraform) and automated environment provisioning in Azure or AWS.
Lead engineers in deploying and maintaining enterprise platform tools such as Starburst, Immuta, Collibra, Databricks, Synapse, and related services.
Develop plans for cloud migrations and deployments and execute modernization strategies for application and data workloads.
Build and maintain data pipelines and supporting services that enable analytics, ML, and AI-enabled applications.
Partner with stakeholders to shape practical approaches to AI agents, AI infrastructure, and AI application enablement (e.g., orchestration patterns, retrieval/knowledge integration, evaluation and monitoring) in a secure environment.