AI Agent & LLM Developer
Budget: $50 – $0 USD
I’m looking to bring an experienced engineer onto our team to take the lead on building production-ready AI agents that specialise in content-generation use cases.
Your core mission will be to design, train and iteratively improve large-language-model-based agents so they can deliver human-quality outputs inside client products. Expect to work hands-on with model fine-tuning, prompt engineering, tool-calling chains and retrieval-augmented generation, then wrap everything in clean, testable Python (LangChain or similar) so deployment into cloud-native pipelines is straightforward.
Because most projects land with partial datasets, you will also need to advise on minimal-viable data preparation, evaluate open-source checkpoints versus custom training, and document best practices so in-house teams can maintain what you build. Clear communication is essential; our clients rely on concise progress updates and well-structured repos (git, Docker, CI).
Deliverables
• End-to-end agent codebase with reproducible environment files
• Fine-tuned model weights (or hosted endpoints) demonstrating target content quality metrics
• Integration guide and API references
Acceptance criteria will be defined per project, but in every case the agent must run autonomously, handle edge-cases gracefully and include unit tests that pass in CI. If this aligns with your skill set, tell me briefly about one agent you’ve shipped and the stack you used—then let’s discuss first milestones.
Your core mission will be to design, train and iteratively improve large-language-model-based agents so they can deliver human-quality outputs inside client products. Expect to work hands-on with model fine-tuning, prompt engineering, tool-calling chains and retrieval-augmented generation, then wrap everything in clean, testable Python (LangChain or similar) so deployment into cloud-native pipelines is straightforward.
Because most projects land with partial datasets, you will also need to advise on minimal-viable data preparation, evaluate open-source checkpoints versus custom training, and document best practices so in-house teams can maintain what you build. Clear communication is essential; our clients rely on concise progress updates and well-structured repos (git, Docker, CI).
Deliverables
• End-to-end agent codebase with reproducible environment files
• Fine-tuned model weights (or hosted endpoints) demonstrating target content quality metrics
• Integration guide and API references
Acceptance criteria will be defined per project, but in every case the agent must run autonomously, handle edge-cases gracefully and include unit tests that pass in CI. If this aligns with your skill set, tell me briefly about one agent you’ve shipped and the stack you used—then let’s discuss first milestones.