REMOTE (INDIA): AI Engineer- SaaS Platform
Marrina Decisions
リモート勤務
- 勤務地
- Remote (India)
- 勤務時間
- Full Time
- 掲載日
- 2026年10月18日
応募する前に、企業のウェブサイトで現在も募集中かどうかと詳しい条件を確認してください。
仕事内容
操作画面は日本語です。企業が掲載した求人の職種名や説明文は原文のまま表示され、英語の場合があります。
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