Top Hybrid Software Engineer Jobs

Lead a senior engineering team building customer-facing products, AI-powered analytics, and infrastructure. This hands-on manager will guide architecture, product execution, engineering practices, team growth, and AI-enabled software development. The role requires strong technical judgment, ownership, autonomy, and experience leading senior engineers in a high-trust environment.

Develop and administer Microsoft Power Platform and SharePoint Online solutions for complex government workflows. Build Power Apps, automate processes with Power Automate, create Power BI dashboards, integrate Microsoft 365 and Azure services, gather requirements, and provide Tier 2/3 technical support. The role requires an active Secret clearance and experience with Dataverse, connectors, SharePoint permissions, workflow monitoring, and error handling.

Leads KBRA’s Business Applications function, owning enterprise SaaS strategy, governance, lifecycle management, integrations, identity access, operational support, and application modernization. The manager administers platforms such as Microsoft 365, Slack, Zoom, Box, and Atlassian; configures Entra ID SSO and provisioning; manages vendors and escalations; develops roadmaps and metrics; partners with business and technology stakeholders; and mentors a small team while remaining hands-on in engineering, troubleshooting, automation, and project delivery.

Designs, builds, and enhances a full-stack data platform covering data ingestion, ETL, big data processing, APIs, and web and mobile applications. Responsibilities include writing tested code, solving technical and data challenges, developing AI-integrated tools, transforming large datasets, and automating repetitive processes. The role also involves collaboration with Product and Design teams and participation in squad rotation.

Lead and co-develop the Maven Voice Agent platform, managing engineers while guiding architecture, product execution, and technical standards. Oversee real-time voice infrastructure, conversational AI, telephony integrations, agent orchestration, customer workflows, reliability, latency, scalability, observability, and production operations. Partner with Product, Design, AI/ML, Customer Success, and enterprise customers to deliver reliable voice experiences and continuously improve engineering processes and platform capabilities.

Develop and maintain front-end, back-end, or full-stack features for an AI-native SaaS platform. Contribute to AI-powered capabilities, client deployments, integrations, troubleshooting, performance, reliability, and scalability. Collaborate with product, design, customer success, data, and international engineering teams while participating in code reviews, documentation, and continuous improvement.

Supports and enhances ATIS/ATMS systems for a 24/7 transportation management center. Develops Python and Java applications, web tools, APIs, dashboards, reports, database solutions, automation, and integrations. Maintains applications across Windows and Linux environments, troubleshoots operational issues, manages deployments and documentation, and supports system improvements involving analytics, AI, GIS, and ITS technologies. Provides technical guidance, training, and coordination with transportation personnel, vendors, and engineering teams.

Own enterprise customer onboardings from signed agreement through stable production launch. Integrate ERP and CRM systems, clean and reconcile messy financial data, ship production TypeScript integrations, improve AI agent prompts and evaluations, and convert customer learnings into product improvements and scalable onboarding playbooks. The role combines software development, customer-facing technical ownership, and building the company’s forward-deployed engineering function.

Build and deploy internal AI agents, applications, data pipelines, integrations, and operational workflows used across Product, Engineering, Deployments, GTM, and Operations. Partner with cross-functional stakeholders to automate recurring work, improve deployment and release processes, coordinate labeling operations, support technical commercial enablement, and contribute to pricing and product analysis.

Optimize GPU kernels and data-plane performance for large-scale AI workloads by profiling, identifying bottlenecks, tuning kernels, developing benchmarks and tooling, and collaborating with ML and platform teams to maximize GPU utilization, throughput, and reliability.