Senior Machine Learning Engineer - Document Processing​/Production AI Systems

Company Overview

Pantheon Data (a Kenific Holding company) is a private, small business based in the Washington, DC, area. Pantheon Data was founded in 2011, initially providing acquisition and supply chain management services to the US Coast Guard. Our service offerings have grown in the past ten years, including infrastructure resiliency, contact center operations, information technology, software engineering, program management, strategic communications, engineering, and cybersecurity.

We have also grown our customer base to include commercial clients. The company has used this experience to expand our service offerings to other agencies within the Department of Homeland Security (DHS), the Department of Defense (DoD), and other Federal Civilian Agencies.

We are seeking a Senior Machine Learning Engineer to help build production-oriented AI and Intelligent Document Processing (IDP) systems. This role is for a hands-on engineer who can move beyond experiments and build working software that ingests, processes, analyzes, retrieves, and explains information from complex unstructured and semi-structured sources.

The ideal candidate has real depth in machine learning, NLP, OCR, computer vision, LLMs, and retrieval-based systems, but also has the broader engineering judgment to understand the system around the model: data pipelines, APIs, databases, cloud infrastructure, containers, testing, evaluation, observability, and production failure modes.

This is not a notebook-only, prompt-only, or research-only role. A successful candidate should be prepared to discuss specific systems they have built, including the data flow, model or inference architecture, deployment approach, evaluation strategy, failure modes, and what they personally implemented.

• Design and build AI/ML capabilities for Intelligent Document Processing, including OCR post-processing, document parsing, NLP/LLM extraction, semantic search, retrieval, evidence grounding, and structured data generation.
• Develop production-quality Python services, pipelines, and tooling that turn messy source documents into reliable, traceable, usable information.
• Work across the full lifecycle of AI systems: data ingestion, preprocessing, model or LLM integration, evaluation, deployment, monitoring, and iterative improvement.
• Build and improve systems that process PDFs, scanned documents, tables, forms, drawings, images, technical manuals, and other complex document types.
• Collaborate with software engineers, data engineers, cloud engineers, product leads, customers, and leadership to turn ambiguous technical problems into working solutions.
• Make practical engineering decisions about when to use deterministic logic, classical NLP, OCR, embeddings, LLMs, fine-tuned models, or human review workflows.
• Help establish engineering standards for evaluation, reproducibility, model behavior, data quality, traceability, and responsible use of AI in customer-facing systems.

• Design, implement, and maintain ML/AI software components for IDP and Generative AI systems.
• Build data pipelines for unstructured and semi-structured data, including document ingestion, extraction, cleaning, enrichment, validation, and storage.
• Develop and evaluate NLP, OCR, computer vision, embedding, retrieval, and LLM-based approaches for document understanding use cases.
• Create APIs, internal tools, review interfaces, dashboards, or validation workflows that allow engineers and users to inspect, correct, and trust system output.
• Contribute production-quality code with clear structure, tests, logging, error handling, and documentation.
• Deploy and support ML/AI services in cloud or containerized environments, including model serving, batch processing, and workflow automation.
• Design evaluation approaches for extraction quality, retrieval quality, model behavior, hallucination risk, and end-to-end system performance.
• Troubleshoot system behavior across model output, data quality, retrieval, schema design, infrastructure, latency, cost, and user workflow issues.
• Mentor other engineers and help raise the technical quality of the team.
• Communicate clearly with both technical and non-technical stakeholders, including project managers, customers, and executive leadership.

• Bachelor's degree in Computer Science, Engineering, or a related technical field from an ABET accredited university.
• 5+ years of professional hands-on experience in machine learning engineering, AI engineering, data science engineering, or a closely related software engineering role. Plus an additional 5 years of experience in technology and software engineering.
• Demonstrated experience building AI/ML systems beyond notebooks, prototypes, or demos. Candidates should have shipped or supported pipelines, services, APIs, inference endpoints, evaluation harnesses, or production-facing tools.
• Strong Python engineering experience, including readable, maintainable code; debugging;…