Top Artificial Intelligence Software Engineer Jobs

Design and build scalable, secure cloud-native backend systems in AWS. Implement event-driven architectures, async concurrent processing, data pipelines, serverless services, and REST APIs (Flask/FastAPI). Ensure performance, caching, testing, debugging, and cloud security (IAM, encryption, authentication).

Lead end-to-end architecture and pre-sales for SAP Cloud ERP (especially S/4HANA Cloud) transformations. Drive integration, extensibility, cloud/on-prem strategies, workshops, demos, executive briefings, and coach teams while aligning technical design to business value and influencing product roadmaps.

Technical co-founder responsible for building and hardening an LLM-powered AI front desk: production voice and workflow systems, delivery infrastructure, unit-economics, observability, multi-tenant security, customer-facing technical representation, and hiring the founding engineering team to scale the managed Role-as-a-Service platform.

Full-stack engineer owning feature development from design through deployment. Collaborate with product and design, make tradeoffs, deliver measurable technical outcomes, and work through ambiguity to drive business impact.

Own and improve backend case execution systems (eligibility, batching, submissions, orchestration). Solve complex, data-driven decision and workflow problems, collaborate cross-functionally, and support internal UI/admin tooling as needed.

The Intelligent Systems Platform Engineer builds and operates Hyper's AI-native Industrial IoT platform, managing data ingestion, pipelines, and analytics alongside data scientists and product managers.

This role involves building early versions of the Silkline platform, leading product development, designing API integrations, and strategizing the technical direction while managing an engineering team.

Design, build, and scale backend services powering search, recommendations, and retrieval. Develop data pipelines to ingest, normalize, and index large government and enterprise datasets. Create embedding-based retrieval pipelines, resolve entities in messy data, and own projects end-to-end from design through deployment, collaborating with product and engineering teams.

Design, build, and maintain scalable ELT/ETL and streaming data pipelines into cloud warehouses. Implement transformations and dimensional models (dbt), ensure data quality and observability, write SQL for analysis, support BI/dashboarding, monitor SLAs, troubleshoot incidents, participate in on-call rotations, and collaborate cross-functionally to deliver analytics-ready datasets.

Build and scale the core product using Python, while ensuring performance and reliability, in collaboration with founders in a fast-paced environment.