AI Engineer - Public Sector

Unstructured

United States

Vor Ort

USD 100.000 - 130.000

Vollzeit

14 Tage+
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Benefits dieser Stelle

Medical, dental, and vision coverage
401(k) with company match
Unlimited PTO
$500 work-from-home stipend
$70/month internet reimbursement

Zusammenfassung

Unstructured is seeking an AI Engineer to transform messy data into AI solutions. The successful candidate will own the lifecycle of AI solutions, from research to AWS deployment, ensuring systems are scalable and performant. Responsibilities include designing production-grade RAG pipelines and evaluating new AI models.

A strong background in Python and experience with AWS GovCloud environments are essential. Employees enjoy benefits including comprehensive insurance, unlimited PTO, and a supportive work environment.

Qualifikationen

  • Proven experience deploying Production RAG pipelines against real‑world, messy datasets.
  • Strong Python engineering skills—writing clean, scalable, and maintainable code.
  • Experience operating within AWS/GovCloud environments.

Aufgaben

  • Own the lifecycle of AI solutions from initial research to AWS deployment.
  • Design and implement production‑grade RAG pipelines and agentic workflows using Python.
  • Evaluate new models and prototype approaches for government deliverables.

Kenntnisse

Python
AI Model Deployment
RAG Pipeline Expertise
AWS GovCloud

Tools

LangChain
Elasticsearch
Docker

Jobbeschreibung

Mission

At Unstructured, we are obsessed with transforming messy, unstructured data into a format that LLMs can actually use. Our Public Sector team has recently secured several high‑impact contracts that demand more than just "off‑the‑shelf" solutions. We are looking for an AI Engineer who thrives at the intersection of R&D and production‑grade software engineering.

Responsibilities
  • You will be a high‑agency individual contributor, owning the lifecycle of AI solutions from initial research to AWS deployment.
  • 50% Building & Shipping: Design and implement production‑grade RAG pipelines and agentic workflows using Python. You’ll build systems that handle real‑world "messy" data (PDFs, scanned docs, images, full motion video) and ensure they are performant and scalable.
  • 30% Research & Experimentation: Stay at the bleeding edge. You’ll evaluate new models (LLMs, embedding models, object detection), prototype approaches for SBIR/government deliverables, and run experiments to prove what actually works.
  • 20% Strategy & Collaboration: Partner with the team to document architectures, contribute to technical reports for contract deliverables, and participate in pre‑sales calls to architect solutions for complex client needs.
Qualifications

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.

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.
Must‑Haves
  • 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.
Nice‑to‑Haves
  • 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.
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.
Benefits

You’ll be surrounded by smart, kind, low‑ego people who genuinely enjoy building together. We invest in our team with company offsites, best‑in‑tech swag, and the tools you need to do your best work, wherever you’re based.

We support you holistically, not just at work. From medical, dental, and vision coverage effective the 1st of the month following your start date, life and disability insurance, unlimited PTO, and flexible parental leave, to a 401(k) with company match, equity, a $500 work‑from‑home stipend, $70/month internet reimbursement, and team/company offsites throughout the year—we want you focused on building, growing, and staying energized for the long haul.

Most importantly, we’re scaling fast. This is a chance to join early enough to build foundational technology, influence how we operate, and help shape the future of a company defining the next era of AI infrastructure.

If you're excited about what we're building, we'd love to meet you.

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