(INDIA): AI Engineer- SaaS Platform

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.