Forward Deployed Engineer Job Details | Aurigo Software Technologies

Aurigo Software Technologies is a company that provides AI-native platforms for capital owners. They are seeking a Forward Deployed Engineer who will be responsible for configuring AI agents in customer environments and developing custom machine learning models for capital program data.

Responsibilities:

• 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
• 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:

• 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
• 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