Job description

Job Summary: This position is responsible for designing, delivering, and owning complex, production-grade enterprise applications and artificial intelligence systems that support core business operations. This role 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 operates with a high degree of autonomy, making architectural and technical decisions while ensuring solutions are reliable, measurable, secure, auditable, and aligned with enterprise standards. • Responsibilities: Designs and owns end to end systems, including data models, backend services, APIs, web applications, and mobile applications. Builds offline capable mobile and web applications for users working in remote environments with limited or no connectivity, including data synchronization, queuing, and conflict resolution. Develops applications supporting field operations, including labor time entry, production tracking, daily reporting, and equipment usage. Develops applications supporting pricing, proposal development, rate libraries, and historical pricing analysis. Develops applications supporting safety and compliance, including observations, hazard assessments, incident reporting, corrective actions, and training records. Develops financial models and planning applications, including budgeting, forecasting, profitability modeling, and period close workflows with versioning and audit history. Develops configurable data capture tools supporting photos, video, location, barcode scanning, and signatures. Establishes and maintains canonical data models for projects, cost structures, employees, equipment, customers, and vendors. Designs and owns AI solutions, including LLM based applications, classical machine learning models, and hybrid approaches. Builds and operates complex AI systems such as agentic workflows, retrieval augmented generation platforms, document extraction, anomaly detection, and natural language reporting. Evaluates ambiguous business problems and determines appropriate architectures, patterns, and modeling techniques. Defines and implements evaluation strategies, metrics, and feedback loops to measure model effectiveness and business impact. Leads the development of data pipelines, integrations, warehouse models, and evaluation datasets required to support enterprise systems. Integrates with enterprise systems including ERP, payroll, business intelligence, telematics, and collaboration platforms, and implements automated reconciliation and data integrity controls. Owns production readiness, including performance, reliability, cost, observability, and failure handling. Identifies and mitigates risks related to bias, hallucination, data leakage, and unintended AI behavior. Plans and executes system migrations, including data migration, parallel operation, validation, and rollback. Collaborates cross functionally with operations, project management, accounting, human resources, and safety to define requirements and guide solution design. Mentors and provides technical guidance to other team members. Contributes to the establishment and evolution of engineering standards, patterns, documentation, and best practices. • Qualifications: 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 of 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. Experience in construction, utilities, field service, or manufacturing environments preferred. Bilingual English and Spanish preferred.

Requirements

Must-haves

Designs and owns end to end systems, including data models, backend services, APIs, web applications, and mobile applications.
Builds offline capable mobile and web applications for users working in remote environments with limited or no connectivity, including data synchronization, queuing, and conflict resolution.
Develops applications supporting field operations, including labor time entry, production tracking, daily reporting, and equipment usage.
Develops applications supporting pricing, proposal development, rate libraries, and historical pricing analysis.
Develops applications supporting safety and compliance, including observations, hazard assessments, incident reporting, corrective actions, and training records.
Develops financial models and planning applications, including budgeting, forecasting, profitability modeling, and period close workflows with versioning and audit history.
Develops configurable data capture tools supporting photos, video, location, barcode scanning, and signatures.
Establishes and maintains canonical data models for projects, cost structures, employees, equipment, customers, and vendors.
Designs and owns AI solutions, including LLM based applications, classical machine learning models, and hybrid approaches.
Builds and operates complex AI systems such as agentic workflows, retrieval augmented generation platforms, document extraction, anomaly detection, and natural language reporting.
Evaluates ambiguous business problems and determines appropriate architectures, patterns, and modeling techniques.
Defines and implements evaluation strategies, metrics, and feedback loops to measure model effectiveness and business impact.
Leads the development of data pipelines, integrations, warehouse models, and evaluation datasets required to support enterprise systems.
Integrates with enterprise systems including ERP, payroll, business intelligence, telematics, and collaboration platforms, and implements automated reconciliation and data integrity controls.
Owns production readiness, including performance, reliability, cost, observability, and failure handling.
Identifies and mitigates risks related to bias, hallucination, data leakage, and unintended AI behavior.
Plans and executes system migrations, including data migration, parallel operation, validation, and rollback.
Collaborates cross functionally with operations, project management, accounting, human resources, and safety to define requirements and guide solution design.
Mentors and provides technical guidance to other team members.
Contributes to the establishment and evolution of engineering standards, patterns, documentation, and best practices.
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.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learningpythontypescriptsql

Platform: adp-workforce-now
Effort: medium