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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