Artificial Intelligence Engineer III

Insperity

About The Position

This position is responsible for designing, delivering, and owning complex, production grade artificial intelligence systems that address ambiguous and high impact business problems. This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. The AI Engineer III operates with a high degree of autonomy, making architectural and technical decisions while ensuring solutions are reliable, measurable, secure, and aligned with enterprise AI standards.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field, or equivalent practical experience.
Five or more years of professional experience spanning software engineering, data science, machine learning engineering, or AI system development.
Advanced proficiency in software engineering, including Python, APIs, testing, and system design.
Advanced proficiency in one or more large scale data platform such as Snowflake, Databricks, Amazon Redshift, Microsoft 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 deploying, monitoring, and iterating on AI systems in production environments.
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.

Responsibilities

Designs and owns end-to-end 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 (RAG) platforms, and decision-support solutions.
Evaluates ambiguous business problems and determines appropriate AI patterns, modeling techniques, and system architectures.
Defines and implements evaluation strategies, metrics, and feedback loops to measure model effectiveness and business impact.
Leads the development of data pipelines, feature strategies, and evaluation datasets required to support AI systems.
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.
Collaborates cross-functionally with product management, platform teams, and stakeholders to guide solution design and delivery.
Mentors and provides technical guidance to other team members.
Contributes to the establishment and evolution of AI engineering standards, patterns, and best practices.