Join our team to develop cutting-edge AI capabilities, focusing on LLM orchestration, retrieval, and applied machine learning. This role involves delivering production-ready services for structured data generation, information extraction, ranking, and analytics, covering the entire lifecycle from concept to deployment.

We are seeking professionals to implement sophisticated LLM workflows, including advanced prompting techniques, function/tool calling, and agentic steps. You will integrate hybrid Retrieval-Augmented Generation (RAG) queries, ensuring accuracy through grounding verification and proper citations.

This position requires developing machine learning models for classification, extraction, reranking, and basic forecasting, with a strong emphasis on rigorous evaluation using appropriate metrics. You will also build robust APIs for seamless application integration and create structured outputs such as PPTX, DOCX, and PDF documents from JSON data.

Implement advanced LLM workflows utilizing prompting, function/tool calling, and agentic steps. Integrate hybrid RAG queries with grounding verification and citations for enhanced accuracy.

Develop machine learning models for classification, extraction, reranking, and light forecasting, supported by appropriate evaluation metrics. Build structured generation capabilities (e.g., JSON to PPTX/DOCX/PDF/diagrams) and reliable APIs for application integration.

Create comprehensive evaluation sets and automated checks to ensure quality, grounding, latency, and cost-effectiveness, iterating based on telemetry data. Collaborate closely with data engineering teams on indexing strategies, embedding techniques, and optimizing retrieval performance.

A Bachelor's or Master's degree in a relevant field is required.

Possess over 9 years of strong experience in Python, including frameworks like FastAPI, Pydantic, and libraries such as Pandas/Polars. Expertise in LLM frameworks like LangChain or Semantic Kernel, with a focus on function calling and structured outputs, is essential.

Demonstrate experience with cloud-based model hosting on AWS, GCP, or Azure (SageMaker, Vertex AI, Azure ML), including embeddings and vector search capabilities. Proficiency in programmatic document generation using libraries like python-pptx, python-docx, and reportlab, along with diagramming tools such as Mermaid or Graphviz, is also necessary.

Apply practical machine learning skills for extraction and classification tasks, understanding evaluation methodologies and productionization patterns.