Top Entry Level Artificial Intelligence Software Engineer Jobs

Build Mesh’s financial governance platform from the ground up, partnering with founders and customers to architect and ship backend infrastructure, AI agentic workflows, enterprise integrations, data pipelines, spreadsheet interpretation, evaluation systems, and auditability tooling. The role owns problems end-to-end, from customer discovery and product design through production deployment and iteration, while operating across AI systems, developer tooling, and enterprise software.

Build internal software for field collection operations, including tasking, site setup, device inventory, shift tracking, local tools, and site-health monitoring. Improve operational efficiency by observing field workflows, removing bottlenecks, and making site expansion repeatable. Integrate field tools with data infrastructure and marketplace incentives, while collaborating closely with on-the-ground teams and occasionally traveling to field sites.

Entry-level engineering trainees will contribute across the software and data development lifecycle, including requirements analysis, architecture, coding, testing, deployment, documentation, and maintenance. They will gain hands-on experience with cloud, data engineering, LLMs, prompt engineering, autonomous AI agents, ETL/ELT pipelines, distributed data systems, APIs, CI/CD, and software quality practices. The role requires collaboration, problem-solving, communication, adaptability, and full-time work from the Schaumburg office.

Build, maintain, and optimize internal systems and AI-powered automations, including a company knowledge-base RAG system, affiliate tracking and payments, lead-to-sales workflows, customer support and CSAT tools, automated call analysis, dashboards, and HubSpot ticket creation. The role requires practical Next.js and Python knowledge, experience developing internal workflow tools, and familiarity with AI agents.

Build and scale end-to-end product features across frontend, backend, data, and infrastructure. Own projects from discovery through launch and iteration, design reliable systems, improve engineering foundations, and deliver polished user experiences. Collaborate closely with founders, product, and design while shaping technical direction and engineering culture. The role emphasizes full-stack development, 0-to-1 products, AI tools, scalability, observability, testing, release processes, and high ownership.

DeployCo invites established teams of two to five people to apply together for enterprise AI deployment roles. Teams will build agentic applications and workflow tools with OpenAI models, work directly with customers, make architecture and reliability decisions, prototype and productionize solutions, and improve reusable deployment practices. Members are assessed and hired individually into roles aligned with their skills. Applicants should have delivered complex products or systems together and demonstrate strong technical, product, data, AI, or delivery capabilities.

Develop and maintain scalable full-stack applications using ReactJS and Java Spring Boot APIs. Build RESTful backend services, optimize Microsoft SQL Server queries and schemas, and ensure application performance, reliability, and data integrity. Translate business requirements into technical solutions, write maintainable code, and collaborate with product, QA, UX, DevOps, vendors, and engineering teams within an agile software development environment.

Develop and scale Lightning AI’s backend platform using Go, spanning APIs, infrastructure, billing, security, and integrations. Own features end to end, design reliable and scalable systems, improve architecture and performance, maintain software quality and continuous delivery, reduce technical debt, and mentor engineers. Collaborate with engineering, product, and design teams in a fast-changing SaaS environment.

Build and maintain a React/TypeScript operations portal and Supabase/PostgreSQL backend for autonomous aircraft medical logistics. Improve aircraft allocation, mission generation, telemetry ingestion, live dashboards, reliability, testing, logging, and observability. Integrate Python services with ArduPilot aircraft and simulations while collaborating with autonomy and simulation engineers to mature real-flight and digital-twin systems.

Build and operate Cognitiv’s core backend platform for real-time bidding and campaign execution. Own features end to end from design through deployment, write reliable and well-tested production code, improve observability and service reliability, participate in on-call and post-mortems, and contribute to system design. Collaborate across teams, facilitate planning, mentor junior team members, and help evolve high-scale campaign management systems.