Backend AI Engineer

Nexxa is building the best AI systems for heavy industries — enabling machines, systems and operations to think, decide and act autonomously across manufacturing, large-scale infrastructure, logistics and legacy environments.
Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.

Role Overview

We're looking for Backend AI Engineers to design, build, and own the core AI infrastructure and services that power Nexxa's products in production. Where our Forward Deployed Engineers embed with customers to deliver solutions on the ground, this role builds the systems that make those solutions possible at scale: model-serving pipelines, inference and orchestration layers, data pipelines, and the APIs and microservices that connect Generative AI, Computer Vision, and Machine Learning models to real enterprise environments.

This role is a blend of backend software engineering, ML infrastructure, and systems architecture. You'll design distributed systems, integrate and serve models in production, and build the reusable platform capabilities that our customer-facing and product teams depend on.

Key Responsibilities

• Design, build, and maintain backend services and APIs that power GenAI, LLM, and Computer Vision model integrations across Nexxa's products.

• Build and own core AI/ML infrastructure: model-serving pipelines, inference services, data pipelines, and embedding/vector stores.

• Architect scalable, production-grade systems for real-time and batch AI workloads across manufacturing, infrastructure, and logistics domains.

• Implement and optimize RAG systems, prompt/context pipelines, and orchestration layers connecting models to enterprise and operational data sources.

• Build robust APIs, microservices, and integration layers connecting AI systems to customer data, legacy systems, and existing infrastructure.

• Own the reliability, performance, and observability of backend AI systems — logging, monitoring, testing, and CI/CD for ML services.

• Collaborate closely with Forward Deployed Engineers, ML engineers, and product teams to translate customer and field requirements into reusable, hardened backend capabilities.

• Evaluate and integrate ML/CV/LLM models into production backend systems; manage model versioning, rollout, and deployment pipelines.

• Produce clear technical documentation: architecture diagrams, API specs, and runbooks for internal and customer-facing teams.