Job description

Nexxa nexxa.ai 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. - Mentor engineers and contribute to internal backend engineering best practices. QUALIFICATIONS - 4–8+ years of experience in backend software engineering, ML/platform engineering, or similar roles. - Strong proficiency in TypeScript/Node.js (our primary backend language), with strong API and microservice design skills. Working proficiency in Python is a plus for ML/model integration work. - Hands-on experience building and operating production backend systems at scale — distributed systems, databases, message queues. - Experience integrating ML or Generative AI models (LLMs, multimodal models) into backend services — inference, orchestration, and evaluation. - Solid understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). - Experience designing and operating data pipelines (batch and/or streaming) across structured and unstructured data. - Hands-on experience building retrieval-augmented generation (RAG) systems and AI memory architectures — retrieval pipelines, vector stores, context management, and long-term/session memory for LLM applications. - Strong grasp of system design fundamentals: scalability, reliability, security, and observability. - Comfortable working cross-functionally with ML engineers, product, and customer-facing teams. - Bachelor's degree (or higher) in Computer Science or a related field. PREFERRED - Familiarity with ML frameworks (PyTorch, TensorFlow, OpenCV) sufficient to integrate, serve, or evaluate models, even without training them yourself. - Experience with MLOps tooling: model registries, feature stores, CI/CD for ML, and monitoring/observability for ML systems. - Background in event-driven or real-time systems (Kafka, gRPC, WebSockets). - Experience in industrial, IoT, or operational technology (OT) environments. - Experience in startup or high-growth environments. WHAT WE'RE LOOKING FOR - A backend engineer who wants to build the infrastructure powering real-world autonomous AI systems. - Someone who can architect for scale and reliability while still moving fast. - A systems thinker who enjoys turning ambiguous AI capabilities into dependable, production-grade backend services. - A strong collaborator who partners well with ML engineers, Forward Deployed teams, and product. WHY JOIN NEXXA.AI nexxa.ai? Innovative Environment: Build the foundational systems behind groundbreaking AI and automation technologies transforming heavy industries. Collaborative Culture: Be part of a team that values innovation, discipline, and continuous improvement. Professional Growth: Benefit from significant opportunities for career development and advancement. Competitive Compensation: Enjoy a comprehensive salary and equity package reflective of your expertise and contributions. If you're passionate about backend engineering and eager to build the infrastructure powering advanced AI solutions, we'd love to connect.

Requirements

Must-haves

Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.
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.
Mentor engineers and contribute to internal backend engineering best practices.
4–8+ years of experience in backend software engineering, ML/platform engineering, or similar roles.
Hands-on experience building and operating production backend systems at scale — distributed systems, databases, message queues.
Experience integrating ML or Generative AI models (LLMs, multimodal models) into backend services — inference, orchestration, and evaluation.
Solid understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Experience designing and operating data pipelines (batch and/or streaming) across structured and unstructured data.
Hands-on experience building retrieval-augmented generation (RAG) systems and AI memory architectures — retrieval pipelines, vector stores, context management, and long-term/session memory for LLM applications.
Strong grasp of system design fundamentals: scalability, reliability, security, and observability.

Nice-to-haves

Strong proficiency in TypeScript/Node.js (our primary backend language), with strong API and microservice design skills. Working proficiency in Python is a plus for ML/model integration work.
Familiarity with ML frameworks (PyTorch, TensorFlow, OpenCV) sufficient to integrate, serve, or evaluate models, even without training them yourself.
Experience with MLOps tooling: model registries, feature stores, CI/CD for ML, and monitoring/observability for ML systems.
Background in event-driven or real-time systems (Kafka, gRPC, WebSockets).
Experience in industrial, IoT, or operational technology (OT) environments.
Experience in startup or high-growth environments.

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Keywords to mirror

aimachine learningmlllmci/cdapitypescriptnode.jspythonawsgcpazuredockerkubernetes

Platform: ashby
Effort: quick

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