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AI Systems Architect for Next-Gen Mental Healthcare
1 week ago
Kana Health Inc. is on a mission to revolutionize mental healthcare using cutting-edge AI technology.
Our Vision
We aim to create AI-powered platforms that provide clinical risk assessments, patient profiling, and personalized insights for therapists.
The Role
We are seeking an experienced AI/ML Engineer to design and build intelligent AI Agents that combine LLMs, traditional ML models, and retrieval-based models to perform complex tasks.
Key Responsibilities:
* Design & Build AI Agents: Develop AI Agents that integrate LLMs, traditional ML models, and retrieval-based models to perform complex tasks.
* Implement Chaining, Prompting, Fine-Tuning, and Multi-Agent Orchestration Techniques: Implement chaining, prompting, fine-tuning, and multi-agent orchestration techniques for AI-driven decision-making.
* Architect AI Systems: Architect AI systems where multiple models work together for context-aware reasoning.
* Develop & Deploy AI Models: Fine-tune LLMs (LLaMA, Mistral, etc.) for specific use cases in mental healthcare, train and deploy traditional ML models (Scikit-learn, TensorFlow, PyTorch) for predictive analytics, classification, and NLP tasks, implement retrieval-augmented generation (RAG) and leverage vector databases (Pinecone).
* Model Hosting & Inference Optimization: Deploy AI models using Docker, Kubernetes, and serverless AI services (Azure ML, AWS Sagemaker, Google Vertex AI), optimize inference latency using ONNX, TensorRT, vLLM, and DeepSpeed, manage model cost optimization strategies including quantization and pruning.
* Model Lifecycle & Experimentation: Develop AI pipelines for data preprocessing, training, evaluation, and retraining, implement continuous monitoring for AI models (data drift detection, auto-scaling), drive A/B testing and experimentation to optimize AI-powered experiences.
Requirements:
* Hands-on experience in Machine Learning, Deep Learning, or AI Engineering
* Strong Python skills (TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas)
* Experience fine-tuning & deploying LLMs (LLaMA, Mistral, or similar models)
* Experience with AI Agents and multi-model architectures (LLMs + ML models + retrieval-based systems)
* Strong knowledge of model hosting & inference (Docker, Kubernetes, serverless ML services)
* Understanding of Retrieval-Augmented Generation (RAG) and vector databases
* Experience with AI lifecycle management (data pipelines, retraining, drift monitoring)
* Familiarity with multi-agent architectures and AI orchestration
* Experience optimizing model cost & performance (ONNX, vLLM, DeepSpeed, quantization)
Bonus Skills:
* Exposure to graph-based AI, reinforcement learning, or explainable AI (XAI)
* Hands-on experience with high-performance inference optimization
* Familiarity with data engineering tools for AI pipelines
