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Remote role and mid-level experience increase applicant density, but niche LLM/VLM skills moderate competition.
Core ML/LLM production skills transfer across industries, but SaaS and LLM/VLM specifics increase domain bias.
Explicit 3–5 years requirement plus mandatory LLM, production, and tech-stack experience make filters moderately strict.
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Maintain and optimize LLM and VLM powered AI services including content generation, compliance scoring, and campaign testing within the platform.
Manage and scale Flask/FastAPI microservices and Dramatiq queues to ensure high uptime, low latency, and robust asynchronous AI workflows.
Build and maintain feedback loops and monitoring pipelines that improve AI model evaluation, performance, and production stability.
3–5 years of experience as an AI Engineer or Python Backend Engineer in production-grade systems.
Strong Python programming skills with experience in Flask, FastAPI, Uvicorn/Gunicorn hosting, and managing microservice architectures.
Hands-on experience with LLM and VLM models including prompt engineering, fine-tuning, evaluation, and AI infrastructure tools like OpenRouter or equivalents.
Experience in REST API design, Dockerized deployments, CI/CD, logging/monitoring, and asynchronous job queues (Dramatiq or equivalent).
Experienced working on SaaS platforms or AI-first products with live LLM/VLM integrations maintaining production AI pipelines, not just prototypes.
Skilled in building scalable, reliable microservices with a focus on AI model production stability, latency optimization, and cost-quality tradeoffs.
Capable of designing and implementing robust feedback and evaluation systems for AI models to continuously improve output quality in production environments.