- Company Name
- Brain Technologies, Inc.
- Job Title
- Backend AI Engineer
- Job Description
-
**Job Title:** Backend AI Engineer
**Role Summary:**
Design, build, and operate scalable backend services that power an AI platform and its device‑cloud integration. Own the full lifecycle from architecture to deployment, ensuring low‑latency, high‑throughput AI and user‑intent traffic, while maintaining robust infrastructure, observability, and security.
**Expectations:**
- Deliver reliable, real‑time AI services for mobile devices and cloud orchestration.
- Own end‑to‑end infrastructure, from IaC to CI/CD, ensuring fast, safe releases.
- Collaborate across AI/ML, mobile, and systems teams to integrate generative models and data pipelines.
- Participate in on‑call rotations, incident response, and post‑mortems.
- Continuously optimize performance, cost, and reliability of AI inference workloads.
**Key Responsibilities:**
- Architect and implement backend APIs, streaming pipelines, event‑driven services, and vector stores.
- Integrate LLM features (prompt pipelines, rate‑limiting, caching, personalization).
- Own cloud infrastructure for device‑to‑cloud communication, model routing, metrics, and runtime services.
- Build and maintain IaC (Terraform, Ansible) and container orchestration (Docker, Kubernetes, GKE/GCP).
- Design and run CI/CD pipelines for backend and mobile releases.
- Deploy and manage observability stack (logging, metrics, tracing).
- Implement security best practices, secrets management, and compliance.
- Conduct performance profiling, scaling, and cost‑optimization of AI inference.
**Required Skills:**
- Strong programming in Node.js, Go, or Python.
- Experience designing distributed backend systems and scalable REST/GraphQL APIs.
- Proficiency with relational (Postgres, MySQL) and non‑relational (DocumentDB, Redis, vector DBs) databases.
- Containerization and orchestration with Docker/Kubernetes, GKE or equivalent.
- Infrastructure‑as‑Code using Terraform or Ansible on GCP or AWS.
- Familiarity with LLM services (OpenAI APIs, RAG, prompt pipelines).
- Observability tools: Prometheus, Grafana, ELK, OpenTelemetry.
- CI/CD pipeline design and automation.
- Performance profiling, optimization, and cost‑efficient scaling of AI workloads.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Software Engineering, or a related field.
- Relevant certifications (e.g., GCP Professional Cloud Architect, AWS Certified DevOps Engineer, Certified Kubernetes Administrator) are a plus.