Unstructured Technologies Inc. - AI Engineer - Public Sector

• We are looking for a self-directed engineer who excels in high-stakes, ambiguous environments. You are likely a strong fit if your professional background reflects the following: • Systems-First Engineering: You prioritize building reliable, scalable systems over experimental scripts. You have a track record of moving AI models out of notebooks and into production environments where latency, cost, and accuracy are treated as first-class citizens. • Technical Resourcefulness: You are comfortable working in restricted or air-gapped environments. When commercial APIs aren’t an option, you have the expertise to deploy, fine-tune, and optimize open-source models to achieve the mission objective. • Autonomous Problem Solving: You can take a high-level government requirement and translate it into a technical roadmap. You don't require constant oversight to identify the right tool for a job, whether it’s a specific vector database or a custom multimodal pipeline. • A "Generalist" Mindset: While you specialize in AI, you understand the full stack. You are as comfortable discussing embedding strategies as you are configuring AWS GovCloud infrastructure or debugging a FastAPI endpoint. • Proven experience deploying Production RAG pipelines against real-world, messy datasets. • Deep expertise in Agentic system design (tool-use, multi-agent orchestration). • Strong Python engineering skills—writing clean, scalable, and maintainable code • Experience operating within AWS/GovCloud environments. • Experience fine-tuning NLP or object detection models. • Familiarity with LLM evaluation frameworks (hallucination detection, drift monitoring). • Knowledge of government security standards and working in different classification environments and on-prem • Security Clearance: Existing Secret/TS clearance or eligibility is a significant plus. • Your Technical Toolkit • Languages: Python (expert-level), SQL • LLM & Agentic Frameworks: LangChain, LangGraph, CrewAI, or similar orchestration frameworks • RAG Stack: Retrieval with vector databases (Pinecone, Weaviate, Chroma, pgvector), graph databases (Neo4J), Elasticsearch, BM25, and Sentence-Transformers; NLP enrichment with spaCy, GLiNER, and Transformers; optimization using embedding models, reranking pipelines, and DSPy • Evaluation & Observability: RAGAS, DeepEval, Arize Phoenix, and synthetic annotations • Cloud & Infrastructure: AWS, SageMaker, Bedrock, S3, Lambda, Docker, and FastAPI • Data Processing: Complex pipelines for unstructured and multimodal data, including PDFs, scanned documents, images, and audio.