Note: The job is a remote job and is open to candidates in USA. Nexxa is building AI systems for heavy industries, enabling machines, systems, and operations to act autonomously across manufacturing, infrastructure, logistics, and legacy environments. The Backend AI Engineer will build and own production AI infrastructure, including model-serving pipelines, inference and orchestration layers, data pipelines, APIs, and microservices that connect AI models to enterprise systems.
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
Skills
- 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
- 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
Benefits
- Remote work
- Significant opportunities for career development and advancement
- Equity package reflective of your expertise and contributions
Company Overview