> Markdown version of [/jobs/ext/4714902-artificial-intelligence-solutions-engineer](https://www.wearedevelopers.com/jobs/ext/4714902-artificial-intelligence-solutions-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Solutions Engineer - **Company:** Cook Group - **Location:** Bloomington, IN, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, C++ (Programming Language), Code Review, Databases, Continuous Integration, Information Engineering, Python (Programming Language), Key Management, Node.Js, Software Engineering, Systems Integration, TypeScript, Enterprise Data Management, ReactJS, Retrieval-Augmented Generation, Large Language Models, Model Validation, Backend, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Docker, Golang - **Published:** November 14, 2026 - **Apply:** https://www.careerbuilder.com/job-details/artificial-intelligence-solutions-engineer-bloomington-in--72184710-671d-42de-9925-a5b7c339a085 ## About the Role Artificial Intelligence (AI), Artificial Intelligence (AI), Data Science, Software Development, Customer/Client Research, Metrics, Automation, Software Design, Application Programming Interface (API), Model Review, * Bachelor's degree in Computer Science, Engineering, Mathematics, or related field-or equivalent practical experience with strong software engineering fundamentals. * 3+ years building production software or data/ML systems (e.g., full-stack, data engineering, MLOps, or platform engineering). Knowledge, Skills, and Abilities * Proficiency in at least two of: Python, Java, C++, Go, TypeScript/JavaScript; familiarity with testing, code review, and CI/CD. * Hands-on experience with one or more of: LLM application patterns (including RAG), vector databases, model evaluation/monitoring, and deployment of AI systems to production. * Direct experience working with end users/customers-driving discovery, scoping MVPs, presenting to leaders, and iterating in fast feedback loops. Preferred Qualifications: * Background integrating with enterprise data/AI platforms and operational domains where AI augments frontline workflows. * Experience defining product and business health metrics and communicating trade-offs to technical and non-technical audiences. * Familiarity with regulated environments (e.g., healthcare, life sciences) is a plus. ## Description * Own discovery and delivery. Partner with internal and customer stakeholders to define problems, success metrics, and delivery plans; iterate rapidly based on user feedback. * Build production AI applications. Design and implement solutions such as LLM assistants, retrieval-augmented generation (RAG) systems, and intelligent automation-including prompt design, evaluation frameworks, guardrails, and human-in-the-loop workflows. * Make data usable. Develop pipelines and integrations across enterprise systems (APIs, databases, event streams); perform modeling, quality checks, lineage, and governance to enable AI at scale. * Ship full-stack software. Implement reliable backend services (Python/Java/Node/C++ or similar) and practical user interfaces (TypeScript/React) that solve real user problems. * Operate in the cloud. Deploy and run services on AWS/Azure/GCP with containers/orchestration (Docker/Kubernetes), CI/CD, secrets management, monitoring, and observability. * Integrate with enterprise platforms. Connect securely to internal platforms and data sources to accelerate delivery and measurable outcomes for frontline teams. * Engineer for safety and trust. Apply secure-by-design practices: data privacy, access controls, model/feature monitoring, bias and risk assessment, incident response, and auditability for ML/LLM systems. * Measure impact and drive adoption. Instrument products, track adoption/quality/ROI, document decisions, and lead change management through demos, training, and clear communication. * Lead with ownership. Work in small, agile teams; communicate effectively with executives, domain experts, and engineers; take responsibility for high-impact projects."