Top Entry Level Artificial Intelligence Software Engineer Jobs

Lead and grow the platform engineering team while providing hands-on technical direction. Design and build scalable enterprise platform systems covering identity, access management, integrations, extensibility, reliability, security, compliance readiness, and operational excellence. Translate customer needs into technical deliverables, mentor engineers, and guide development in an ambiguous startup environment. AI agent or production LLM experience is beneficial.

Build and help lead Orpex, an AI workforce infrastructure startup. The role is intentionally flexible across engineering, product, technical leadership, and company operations. The hire will define their first 90-day ownership, work directly with the founder, establish engineering and hiring standards, and build the platform for AI-powered service companies. Prior startup founding, CTO, technical founding, product shipping, or service-business operating experience is expected.

Build and operate Origin’s AI observability platform across data-collection agents, backend services, infrastructure, inference pipelines, APIs, and React interfaces. Design distributed systems using AWS, EKS, ClickHouse, and Kafka for high-throughput, multi-tenant production workloads. Develop internal coding agents and define AI-native engineering practices while making decisions around storage, APIs, performance, and reliability.

Build and own software connecting robots to customer businesses, including teleoperation and fleet management interfaces, task scheduling and dispatch, customer system integrations, performance dashboards, and mobile and web tools. The role involves working directly with on-site operators, designing real-time systems, and iterating quickly based on field usage.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Build production AI-agent integrations embedded within enterprise engineering teams. Write code and scripts connecting Harness AI Agents to pipelines, repositories, ITSM tools, security systems, and infrastructure. Collaborate directly with customer engineers, prototype and deploy incrementally, create reusable integration patterns, and support customer transition to independent operation. The role requires strong CI/CD and infrastructure-as-code knowledge, integration experience, stakeholder communication, comfort with ambiguity, and frequent travel to customer sites.

Build and maintain a large case law dataset through document metadata pipelines, web scraper reliability systems, citation-resolution utilities, APIs, and evaluation benchmarks. The role requires backend technical generalism, data pipeline expertise, and basic-to-intermediate infrastructure and DevOps skills. Python and SQL are core, while ML and AI experience is beneficial. This is an in-person position in SoHo, New York City.

Own the workflow engine powering customer runs, including scheduling, queues, retries, batching, idempotency, provider integrations, observability, and cost controls. Scale Postgres and Redis while improving reliability across high-volume data workflows. Work autonomously, use coding agents extensively, and make technical decisions for an early-stage platform.

Own the company’s integration surface, beginning with CargoWise and expanding to Magaya, Descartes, and other operational platforms. Build safe bidirectional write-back connectors with idempotency, replay, reconciliation, validation handling, and workflow safeguards. Integrate deeply with the company’s canonical connector architecture using Python, FastAPI, Celery, PostgreSQL, Redis, and AWS. Apply hands-on TMS and CargoWise/eAdaptor expertise to modify customer systems of record reliably in production.

Develop and maintain full-stack features for a SaaS platform supporting clinical trial workflows. Responsibilities include Python-based application development, frontend collaboration, architecture, testing, code review, refactoring, reliability, data integrity, and regulatory compliance. The engineer will collaborate with product, design, machine learning, infrastructure, clinical, QA, and regulatory teams while contributing to a distributed engineering organization.