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Nexxa.ai is building AI systems for heavy industries—manufacturing, infrastructure, and logistics—enabling autonomous decision-making and operations in complex, legacy environments.
As a Backend AI Engineer, you will design, build, and own the core AI infrastructure and services powering Nexxa's production systems. This role bridges backend software engineering, ML infrastructure, and systems architecture. You'll work on model-serving pipelines, inference and orchestration layers, data pipelines, and APIs connecting generative AI, computer vision, and machine learning models to real enterprise environments.
Key responsibilities include:
- Design and maintain backend services and APIs for GenAI, LLM, and Computer Vision model integrations.
- Build and own AI/ML infrastructure: model-serving pipelines, inference services, data pipelines, and vector stores.
- Architect scalable, production-grade systems for real-time and batch AI workloads across industrial domains.
- Implement and optimize RAG systems, prompt/context pipelines, and orchestration layers connecting models to enterprise and operational data.
- Build robust APIs, microservices, and integration layers connecting AI systems to customer data and legacy infrastructure.
- Own reliability, performance, and observability of backend AI systems—logging, monitoring, testing, and CI/CD for ML services.
- Collaborate with Forward Deployed Engineers, ML engineers, and product teams to translate field requirements into reusable backend capabilities.
- Evaluate and integrate ML/CV/LLM models into production; manage model versioning, rollout, and deployment pipelines.
- Produce technical documentation: architecture diagrams, API specs, and runbooks.
- Mentor engineers and contribute to backend engineering best practices.
Requirements:
- 4–8+ years of backend software engineering, ML/platform engineering, or similar experience.
- Strong proficiency in TypeScript/Node.js (primary backend language) with strong API and microservice design skills; working proficiency in Python preferred for ML/model integration.
- 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 related field.
Preferred qualifications:
- Familiarity with ML frameworks (PyTorch, TensorFlow, OpenCV) sufficient to integrate, serve, or evaluate models.
- 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.
