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.