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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