REMOTE (INDIA): AI Engineer- SaaS Platform
角色概述 我们正在寻找一个人工智能工程师来维护和增强苏特拉平台的人工智能驱动脊柱。这一职位包括确保 LLM/VLM 管道的生产稳定性，优化模型互动，保持 API 和队列，并建立反环节，不断改善 AI 输出。责任 ·维护和优化 LLC 和 VLM 支持服务内容生成，合规评分和竞选测试。·管理和扩展 Flask/FastAPI 微服务，确保高上班时间和低延迟。·维护 Dramatiq 队列为异步 AI 工作流程，竞选活动和管道生成编排。· 在生产环境中部署，监控和调试基于 Uvicorn/Gunicorn 的托管。· 集成与 OpenRouter 及同等 LLM 路由工具来平衡成本，延迟和质量。·设计并完善可靠性，背景意识和合规性的快速工程策略。· 为 AI 模型评估建立和维护反管道 （人在循环中的评分，自动化质量检查，增强）. · 对 AI 服务提供 REST API 进行曝光和维护，确保安全，版本化的终点。· 与后端/前端团队合作以保持微服务架构的协调和维护。· 追踪代币消耗，延迟和错误率以确保生产级性能。所需技能 ·编程：Python 强，具有生产级代码基础的经验。·框架：Flask （用于 API）,FastAPI （可选）,Uvicorn/Gunicorn 为异步托管。·队列/工作人员：Dramatiq （或 Celery/RQ 等效）为背景工作。·AI/ML:与 LLM 和 VLM 实践，包括快速工程，细调和评估。·AI 基础设施：熟悉 OpenRouter 或同等 LLC /VLM 路由和后退工具。·建筑：设计和维护微服务架构。·APIs: • 产品：Dockerized 部署，CI/CD 管道，记录/监控，错误处理。·反循环：构建结构化评估/反系统以实现 AI 模型性能。·云：AWS/GCP 经验优先 （部署，监测，扩展）. 35年的人工智能工程师或 Python 后端工程师与生产系统合作的经验。·以前与 SaaS 平台，LLM/VLM 集成或人工智能第一产品的工作受到高度重视。证明保持人工智能管道在生产中的能力，而不是高估值。
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Role Overview
We are looking for an AI Engineer to maintain and enhance the AI-driven backbone of the Sootra platform. This role involves ensuring production stability of LLM/VLM pipelines, optimizing model interactions, maintaining APIs and queues, and building feedback loops that continuously improve AI outputs.
Responsibilities
- Maintain and optimize LLM- and VLM-powered services for content generation, compliance scoring, and campaign testing.
- Manage and scale Flask/FastAPI microservices, ensuring high uptime and low latency.
- Maintain Dramatiq queues for async AI workflows, campaign generation, and pipeline orchestration.
- Deploy, monitor, and debug Uvicorn/Gunicorn-based hosting in production environments.
- Integrate with OpenRouter and equivalent LLM routing tools to balance cost, latency, and quality.
- Design and refine prompt engineering strategies for reliability, context-awareness, and compliance.
- Build and maintain feedback pipelines for AI model evaluation (human-in-the-loop scoring, automated quality checks, reinforcement).
- Expose and maintain REST APIs for AI services, ensuring secure, versioned endpoints.
- Collaborate with backend/frontend teams to keep microservice architecture aligned and maintainable.
- Track token consumption, latency, and error rates to ensure production-grade performance.
Required Skills
- Programming: Strong in Python, with experience in production-grade codebases.
- Frameworks: Flask (for APIs), FastAPI (optional), Uvicorn/Gunicorn for async hosting.
- Queues/Workers: Dramatiq (or Celery/RQ equivalent) for background jobs.
- AI/ML: Hands-on with LLMs and VLMs, including prompt engineering, fine-tuning, and evaluation.
- AI Infrastructure: Familiar with OpenRouter or equivalent LLM/VLM routing & fallback tools.
- Architecture: Experience designing and maintaining microservice architectures.
- APIs: Strong experience with REST API design (auth, rate limiting, documentation).
- Production: Dockerized deployments, CI/CD pipelines, logging/monitoring, error handling.
- Feedback Loops: Building structured evaluation/feedback systems for AI model performance.
- Cloud: AWS/GCP experience preferred (deployment, monitoring, scaling).
Experience
- 3–5 years as an AI Engineer or Python Backend Engineer working with production systems.
- Prior work with SaaS platforms, LLM/VLM integrations, or AI-first products is highly valued.
Demonstrated ability to maintain AI pipelines in production, not just prototypes.
Originally posted on Himalayas