The AI Engineer will maintain and enhance AI services on the Sootra platform, optimizing pipelines, managing microservices, and developing feedback systems to improve AI performance.
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.
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