AI Engineer

About the job

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.

Requirements

• Python
• LLM/VLM
• API Design

Preferred Technologies

• Python
• LLM/VLM
• API Design