SaaS AI Integration Specialist
Budget: $30 – $250 USD
I’m upgrading my existing SaaS platform with advanced AI capabilities and need a specialist who can handle the full integration stack. The core of the job is to weave large-language-model functionality and a retrieval-augmented-generation (RAG) pipeline into the product, then layer on automation that delivers real value to end-users.
What I need you to build
• A production-ready LLM + RAG service that plugs seamlessly into our current back-end and scales with user demand.
• Three automation tracks—predictive analytics, chatbots/virtual assistants, and automated data processing—each exposed through clean API endpoints.
• Solid integration glue so these AI services talk to the rest of the SaaS features (auth, billing, dashboards, etc.).
I’m flexible on the underlying framework; if you prefer TensorFlow, PyTorch, Hugging Face Transformers, or another proven stack, that’s fine as long as the final service is efficient, secure, and easy to maintain.
Acceptance criteria
1. Endpoints return accurate, low-latency responses under load tests we’ll define together.
2. All code is version-controlled with clear readme and environment setup scripts.
3. A short hand-off session where you demo the workflows inside our staging environment and document configuration steps.
If you’ve shipped similar AI integrations for SaaS products before, I’d love to see your approach and timeline.
What I need you to build
• A production-ready LLM + RAG service that plugs seamlessly into our current back-end and scales with user demand.
• Three automation tracks—predictive analytics, chatbots/virtual assistants, and automated data processing—each exposed through clean API endpoints.
• Solid integration glue so these AI services talk to the rest of the SaaS features (auth, billing, dashboards, etc.).
I’m flexible on the underlying framework; if you prefer TensorFlow, PyTorch, Hugging Face Transformers, or another proven stack, that’s fine as long as the final service is efficient, secure, and easy to maintain.
Acceptance criteria
1. Endpoints return accurate, low-latency responses under load tests we’ll define together.
2. All code is version-controlled with clear readme and environment setup scripts.
3. A short hand-off session where you demo the workflows inside our staging environment and document configuration steps.
If you’ve shipped similar AI integrations for SaaS products before, I’d love to see your approach and timeline.