About the role

Backend AI Engineer building scalable model-serving, RAG, and microservice infrastructure. Powering Nexxa.AI’s autonomous AI systems for manufacturing, infrastructure, logistics, and legacy environments.

Responsibilities

Design, build, and maintain backend services and APIs powering GenAI, LLM, and Computer Vision model integrations
Build and own AI/ML infrastructure including model-serving pipelines, inference services, data pipelines, and embedding/vector stores
Architect scalable, production-grade systems for real-time and batch AI workloads
Implement and optimize RAG systems, prompt/context pipelines, and orchestration layers
Build APIs, microservices, and integration layers connecting AI systems to customer data, legacy systems, and existing infrastructure
Own reliability, performance, and observability of backend AI systems, including logging, monitoring, testing, and CI/CD
Collaborate with Forward Deployed Engineers, ML engineers, and product teams to translate requirements into reusable backend capabilities
Evaluate and integrate ML, CV, and LLM models into production systems; manage model versioning, rollout, and deployment pipelines
Produce architecture diagrams, API specifications, and runbooks
Mentor engineers and contribute to backend engineering best practices

4–8+ years of experience in backend software engineering, ML/platform engineering, or similar roles
Strong proficiency in TypeScript/Node.js
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
Experience with distributed systems, databases, and message queues
Experience integrating ML or Generative AI models, including LLMs and multimodal models, into backend services
Experience with inference, orchestration, and model evaluation
Understanding of AWS, GCP, or Azure
Experience with Docker and Kubernetes
Experience designing and operating batch and/or streaming data pipelines
Hands-on experience building RAG systems and AI memory architectures
Experience with retrieval pipelines, vector stores, context management, and long-term/session memory for LLM applications
Strong understanding of 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 PyTorch, TensorFlow, or OpenCV
Preferred: experience with MLOps tooling, model registries, feature stores, CI/CD for ML, and ML monitoring/observability
Preferred: background in Kafka, gRPC, WebSockets, industrial, IoT, or operational technology environments
Preferred: experience in startup or high-growth environments