Forward Deployed Engineer

THE ROLE:

You are the builder on the deployment team — and that means two distinct but related jobs.

The first is field deployment: configuring Aurigo AI agents inside customer Masterworks environments, integrating the platform with the data systems agencies already run, and solving the technical problems that emerge when enterprise software meets real-world government IT. You work directly with agency data teams, IT staff, and system administrators who control the environments you are deploying into.

The second is model development: building and fine-tuning the ML models that power agents when pre-built AI is not precise enough for capital program data. Infrastructure program management generates specialized structured and unstructured data — cost variance patterns, schedule float analysis, RFI classification, submittal review, daily field reports — and the most valuable agents require custom models trained on that data. You design the data pipelines, engineer the features, run the training cycles, evaluate production performance, and contribute reusable models back to the Aurigo platform.

WHAT YOU'LL DO:

Agent Deployment & Integration
•    Configure and deploy Aurigo AI agents within customer Masterworks environments — tailoring agent behavior, workflows, and outputs to each agency's specific requirements
•    Build and maintain data integrations between Masterworks and agency systems: scheduling tools, cost systems, financial management platforms, document management, GIS, and agency data warehouses
•    Develop scripts and lightweight automation to streamline agency data workflows, reduce manual handoffs, and prepare data for agent consumption
•    Work with agency IT staff, data stewards, and system administrators to navigate access, permissions, and integration constraints in government technology environments
•    Troubleshoot deployment issues in the field — diagnosing root causes, implementing fixes, and documenting solutions for reuse across future deployments

Custom Model Development
•    Design and train custom ML models on capital program data — cost overrun prediction, schedule risk scoring, anomaly detection in project financials, document classification — deployed as intelligence layers inside Aurigo agents
•    Build feature engineering pipelines from Masterworks and connected systems, transforming raw program data into structured, model-ready inputs
•    Fine-tune or adapt large language models for infrastructure-specific tasks: RFI response drafting, submittal compliance review, meeting minute summarization, specification and contract parsing
•    Build data preprocessing pipelines for unstructured construction documents — PDFs, field reports, RFI logs, change order packages — transforming them into structured, model-ready datasets
•    Develop and maintain model evaluation frameworks; monitor production model performance, identify drift, retrain as needed, and document performance metrics for each deployment
•    Contribute models, pipelines, and reusable components back to the Aurigo product team — building the platform's AI capability from field learnings

Requirements:

Required Skills

•    3+ years building and deploying ML models in production — not just notebooks; you have models running in real systems where accuracy and reliability matter
•    Proficiency in Python ML stack: scikit-learn, PyTorch, TensorFlow, or HuggingFace Transformers — you choose the right tool for the problem
•    Experience with NLP techniques applied to document-heavy data: text classification, named entity recognition, embedding models, semantic search
•    Working knowledge of LLM fine-tuning, RAG architecture, or prompt optimization in domain-specific applications
•    Hands-on experience building data pipelines for unstructured or semi-structured data — PDFs, XML exports, structured logs — and transforming them into model-ready features
•    REST API integrations and comfort with the engineering work of connecting enterprise systems
•    Ability to work independently in ambiguous field environments — you diagnose and build without waiting for a perfectly scoped ticket

Preferred Skills

•    Familiarity with MLOps practices: model versioning, evaluation pipelines, monitoring for drift, and retraining workflows in production
•    Experience with construction, infrastructure, or capital program data — cost codes, schedule structures, contract document formats, or similar domain data
•    Prior work in a field deployment, systems integration, or technical consulting role — you have built in client environments under real constraints
•    Familiarity with vector databases (Pinecone, Weaviate, pgvector) or knowledge graph approaches for domain-specific retrieval
•    Experience in government or regulated environments — navigating IT procurement, access controls, and security requirements
•    Public Trust clearance eligibility

About Aurigo

Aurigo provides AI-native platforms that help capital owners connect planning, construction, and operations in a single environment, improving decision-making and execution. Trusted by over 300 organizations across federal, state, and local government, manufacturing, data centers, energy and utilities, and life sciences, we manage more than $450 billion in capital programs across 40,000+ projects in North America. Built on 20+ years of domain expertise, Aurigo has received multiple industry awards and has been certified as a Great Place to Work for three consecutive years.

At Aurigo, we go beyond building technology—we shape the future. If you're excited to join a fast-growing company and collaborate with some of the brightest minds in the industry to solve real-world challenges, let's connect.