Sr. AI Engineer

Key Requirement Overview

• The platform must be completely self-hosted
• No OpenAI or third-party AI API usage allowed
• No recurring SaaS/API costs for core functionalities
• Developers must build and deploy AI capabilities in-house

Required Skill Set

1. AI/ML & LLM Expertise

• Strong experience with open-source LLMs (LLaMA, Mistral, Falcon, etc.)
• Experience in model fine-tuning (LoRA/QLoRA)
• Hands-on with Hugging Face Transformers

2. Model Deployment & Serving

• Experience with model serving tools like vLLM, Ollama, or TGI
• Strong knowledge of GPU-based deployments (CUDA)
• Experience with Docker/containerized deployments

3. Backend Development

• Strong Python expertise
• Hands-on experience with FastAPI and/or Django
• Experience in building scalable APIs and microservices

4. NLP & Moderation (Self-hosted)

• Experience in building NLP classifiers (toxicity, sentiment, spam detection)
• Familiarity with BERT/DistilBERT or similar models

5. Vector Databases & RAG

• Experience with FAISS, ChromaDB, or similar vector databases
• Understanding of embeddings and Retrieval-Augmented Generation (RAG)

6. Social Media API Integration

• Direct integration experience with APIs like Facebook, Instagram, LinkedIn, Twitter, YouTube
• OAuth, rate limiting, and API handling (no third-party aggregators like Ayrshare)

7. DevOps & Self-Hosting

• Experience with Linux servers, Nginx
• Cloud platforms (AWS/GCP/Azure VM-based deployment)
• CI/CD pipelines and deployment handling

Experience Required

• 5–7 years (Mid-level)
• 7–10 years (Senior-level)

Important Note

• Candidates must have practical experience in self-hosted AI systems.
• Profiles primarily dependent on OpenAI or other external APIs should not be considered.