Backend AI Engineer
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Job Description
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
Requirements:
- 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
Benefits:
- Equity package
- Significant opportunities for career development and advancement
- Comprehensive salary and equity package
- Innovative environment focused on AI and automation technologies
- Collaborative culture
- Continuous improvement opportunities
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