Backend AI Engineer

Nexxa.AI; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

Nexxa.AI is building the core backend infrastructure powering Generative AI, CV, and ML-enabled products for manufacturing, infrastructure, and logistics. The Backend AI Engineer will design and own model-serving pipelines, inference layers, and data pipelines at scale.

You’ll implement and optimize RAG workflows, build robust APIs, and ensure observability, security, and reliability across production systems. Strong collaboration with ML engineers and product teams is essential.

Qualifications

• 4–8+ years of backend/software ML or platform engineering.
• Strong TypeScript/Node.js with API/microservice design; Python is a plus.
• Experience building production backend systems at scale (distributed systems, databases, queues).
• Experience integrating ML/Generative AI models into backend services (inference, orchestration).
• Solid understanding of cloud infrastructure (AWS/GCP/Azure) and containers (Docker, Kubernetes).
• Experience designing data pipelines (batch/streaming) across structured/unstructured data.
• Hands-on experience with retrieval-augmented generation (RAG) and vector stores.
• Strong system design, reliability, security, and observability.
• Collaborative with ML engineers, product, and customer teams.
• Bachelor's degree in Computer Science or related field.

Responsibilities

• Design, build, and maintain backend services and APIs powering AI/model integrations.
• Build and own core AI/ML infrastructure: model-serving pipelines, inference services, data pipelines.
• Architect scalable systems for real-time and batch AI workloads across domains.
• Implement RAG, prompt/context pipelines, orchestration layers.
• Build APIs and microservices connecting AI systems to data sources.
• Ensure reliability, performance, and observability of backend AI systems.
• Collaborate with Forward Deployed teams and product groups to translate requirements.
• Evaluate and integrate ML/CV/LLM models into production backend systems.
• Produce architecture diagrams, API specs, and runbooks.
• Mentor engineers and contribute to backend engineering practices.

Skills

Years-experience

TypeScript/Node.js

Python

Distributed systems

ML model integration

Cloud infrastructure

Docker/Kubernetes

Data pipelines

RAG systems

System design

Cross-functional

Education

Bachelor's degree in CS or related

Tools

Docker

Kubernetes

Kafka

gRPC/WebSockets

Job description

Nexxais 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?

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.