Note: The job is a remote job and is open to candidates in USA. Aurigo Software Technologies provides AI-native platforms to help capital owners connect planning, construction, and operations in a single environment. The AI Engineer role involves deploying AI agents in customer environments and developing custom machine learning models tailored to capital program data. The position requires configuring integrations, troubleshooting deployment issues, and building data pipelines and models to enhance the Aurigo platform's AI capabilities.
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
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
- 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
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