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.