Artificial Intelligence Engineer

About the Role We're looking for a Senior AI Engineer to join our team and build the systems powering our generative AI and applied ML products. This is a hands-on role for someone who has shipped production RAG pipelines, fine-tuned and evaluated models, and knows how to take AI features from prototype to scale. Responsibilities Design and build retrieval-augmented generation (RAG) pipelines, including chunking, embedding, and retrieval strategies Architect and optimize vector database infrastructure (Pinecone, Weaviate, pgvector, or similar) for production-scale retrieval Fine-tune, evaluate, and deploy LLMs for specific use cases, including prompt engineering and model evaluation frameworks Build and maintain data pipelines for training, fine-tuning, and continuous evaluation Integrate generative AI features into production applications via APIs (OpenAI, Anthropic, open-source models) Monitor model performance, latency, and cost in production, and iterate on improvements Collaborate with product and engineering teams to translate business problems into AI-driven solutions Stay current with the rapidly evolving AI/ML landscape and evaluate new tools, models, and techniques Required Qualifications 4+ years of professional software engineering experience, with 2+ years focused specifically on applied AI/ML in production Hands-on experience building RAG systems end-to-end (retrieval, chunking, embedding strategies, reranking) Practical experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, or similar) Experience working with LLM APIs (OpenAI, Anthropic, Cohere) and open-source model frameworks (Hugging Face, LangChain, LlamaIndex) Strong Python skills, with experience in ML/data tooling (PyTorch, NumPy, pandas) Understanding of embedding models, semantic search, and evaluation metrics for generative AI quality Experience deploying and monitoring ML/AI systems in production environments Strong grasp of prompt engineering and techniques for improving LLM output reliability Preferred Qualifications Experience fine-tuning open-source LLMs (LoRA, QLoRA, or full fine-tuning) Familiarity with model evaluation frameworks and LLM-as-judge methodologies Experience with agentic AI architectures and tool-use/function-calling systems Background in MLOps (model versioning, CI/CD for ML, experiment tracking — MLflow, Weights & Biases) Experience with multi-modal models (vision, audio) a plus Contributions to open-source AI/ML projects Job Details Type: Full-time Location: Fully Remote Compensation: $30/h - $45/h Show more Show less