Job Function
- Architect and deploy scalable AI systems and MLOps pipelines on AWS, Azure, GCP, or on premise.
- Build end-to-end pipelines for data ingestion, feature engineering, model training, and LLM integration.
- Design multimodal systems combining AutoML, MLOps, and LLM with automation for AI Agent workflows.
- Implement CI/CD using Kubernetes (K8s) with tools like Kubeflow, and Airflow.
- Orchestrate job queues (e.g., RabbitMQ, Celery, Kafka) and automation platforms (e.g., n8n, Dify).
- Validate performance via A/B testing, canary deployments, and continuous monitoring.
Your Impact
- Lead innovative AI Agent builder architecture design.
- Enhance system efficiency, scalability, and reliability for real-time AI Agent applications.
- Set best practices for AI infrastructure and mentor technical teams.
Qualifications
- Bachelor’s in CS, AI, or related field (Master’s/PhD preferred)
- 5+ years in AI system architecture, cloud computing, and distributed systems.
- Expertise in MLOps, AI Agent orchestration, and end-to-end ML pipeline development.
- Proficient with AWS, Azure, GCP, or on premise solutions.
- Strong experience with Kubernetes, CI/CD automation, and container orchestration.
- Familiar with AI/ML/LLM frameworks and tools (HuggingFace Transformers, LangChain, etc.) and configuration management (YAML).
- Bonus: Experience with AI coding assistants (GitHub Copilot, Cursor).
- Skilled in performance tuning, system validation, and monitoring.
Join Us
At MoBagel, you’ll work on state-of-the-art AI solutions, lead impactful projects, and be part of a dynamic, fast-paced startup environment.
