AI Software Developer

PURIS Corporation, LLC•The Woodlands, TX

18d

About The Position

This position designs, delivers, and owns complex, production-grade enterprise applications and artificial intelligence systems that support core business operations. It applies advanced full-stack software, data, and AI engineering expertise to build internal platforms spanning field operations, pricing, safety and compliance, financial models, and structured data capture. The AI Software Developer works with a high degree of autonomy, making architectural and technical decisions while ensuring solutions are reliable, measurable, secure, auditable, and aligned with enterprise standards.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field, or equivalent practical experience.
Eight or more years of professional experience spanning software engineering, data engineering, machine learning engineering, or AI system development.
Advanced full-stack proficiency, including modern web frameworks, a production backend language such as Python, TypeScript, or C#, relational data modeling, SQL, APIs, testing, and system design.
Demonstrated experience building offline-first or local-first applications, including synchronization and conflict resolution.
Production mobile development experience on iOS and Android, including enterprise distribution and device management.
Advanced proficiency in one or more large-scale data platforms such as Snowflake, Databricks, Amazon Redshift, Microsoft Fabric or Synapse, or similar.
Strong applied knowledge of machine learning and artificial intelligence concepts, evaluation techniques, and failure modes.
Hands-on experience designing, building, and deploying LLM-based systems using enterprise-grade platforms and frameworks.
Experience with one or more AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.
Deep understanding of data quality, feature relevance, and model behavior across structured and unstructured data.
Experience building or integrating financial, planning, or forecasting models where accuracy, versioning, and auditability are required.
Experience integrating with an ERP or comparable system of record.
Experience deploying, monitoring, and iterating on systems in production environments.
Working knowledge of enterprise security and access control, including single sign-on, multifactor authentication, role-based access, and handling sensitive employee and business data.
Ability to make and defend technical tradeoff decisions balancing accuracy, cost, risk, and scalability.
Strong communication and leadership skills, with the ability to influence technical direction and mentor others.

Nice To Haves

Experience in construction, utilities, field service, or manufacturing environments.
Bilingual English and Spanish.

Responsibilities

Design and own end-to-end systems, including data models, backend services, APIs, web applications, and mobile applications.
Build offline-capable mobile and web applications for remote users with limited or no connectivity, including data synchronization, queuing, and conflict resolution.
Develop applications for field operations, including labor time entry, production tracking, daily reporting, and equipment usage.
Develop applications for pricing, proposal development, rate libraries, and historical pricing analysis.
Develop applications for safety and compliance, including observations, hazard assessments, incident reporting, corrective actions, and training records.
Develop financial models and planning applications for budgeting, forecasting, profitability modeling, and period-close workflows with versioning and audit history.
Develop configurable data-capture tools supporting photos, video, location, barcode scanning, and signatures.
Establish and maintain canonical data models for projects, cost structures, employees, equipment, customers, and vendors.
Design and own AI solutions, including LLM-based applications, classical machine learning models, and hybrid approaches.
Build and operate complex AI systems, such as agentic workflows, retrieval-augmented generation platforms, document extraction, anomaly detection, and natural-language reporting.
Evaluate ambiguous business problems and determine appropriate architectures, patterns, and modeling techniques.
Define and implement evaluation strategies, metrics, and feedback loops to measure model effectiveness and business impact.
Lead the development of data pipelines, integrations, warehouse models, and evaluation datasets required to support enterprise systems.
Integrate with enterprise systems, including ERP, payroll, business intelligence, telematics, and collaboration platforms, and implement automated reconciliation and data-integrity controls.
Own production readiness, including performance, reliability, cost, observability, and failure handling.
Identify and mitigate risks related to bias, hallucination, data leakage, and unintended AI behavior.
Plan and execute system migrations, including data migration, parallel operation, validation, and rollback.
Collaborate cross-functionally with operations, project management, accounting, human resources, and safety to define requirements and guide solution design.
Mentor and provide technical guidance to other team members.
Contribute to the establishment and evolution of engineering standards, patterns, documentation, and best practices.