AI Engineer

Scigon Solutions, Inc.; Chicago (IL); On-site; USD 72,000 - 96,000; Full time

Job summary

Scigon Solutions, Inc. is hiring an AI Engineer to design, deploy, and operationalize enterprise AI capabilities.

You will work across business partners to translate ideas into production-ready AI solutions, combining hands-on experience with modern LLM platforms and strong software practices. You will drive end-to-end AI enablement, configure foundation models, ensure security and governance, and collaborate with Security, Cloud, and Infrastructure teams to deliver scalable, auditable solutions

Qualifications

• 5+ years of software/automation engineering experience.
• At least 2 years of experience designing and deploying production-grade solutions powered by large language models.
• Strong proficiency in Python development and API-based integrations.
• Experience with enterprise software integration patterns and distributed systems.
• Hands-on experience with modern LLM ecosystems, including prompt engineering, model configuration, tool integration, and function execution.
• Experience working within public cloud environments such as Azure, Google Cloud Platform, Vertex AI, or equivalent technologies.

Responsibilities

• Design, deploy, and manage reusable AI capabilities within enterprise AI platforms, including skills, tools, and plug-in functionality.
• Drive the complete lifecycle of AI enablement solutions, from intake and configuration through deployment, governance, ongoing support, and optimization.
• Configure foundation models such as Claude, Gemini, and similar technologies to support enterprise automation and business workflows.
• Deliver AI solutions from concept through production implementation, ensuring scalability, security, and operational readiness.
• Integrate existing enterprise systems, services, and approved tools using available connectivity frameworks and protocols.
• Collaborate with Security, Cloud, and Infrastructure teams to implement appropriate access controls, identity management practices, and credential governance.
• Ensure solutions comply with established AI governance standards, including auditability, monitoring, risk controls, and operational guardrails.
• Partner with engineering, automation, data, and business teams to identify opportunities and deliver impactful AI‑driven capabilities.

Skills

Python development

API integrations

LLM platforms

Cloud platforms

Stakeholder communication

Self-directed

Tools

Azure

Google Cloud Platform

Vertex AI

Job description

AI Engineer

Pay Rate; $52-$70/hour

Overview

We are seeking an Applied AI Engineer to help design, deploy, and operationalize enterprise AI capabilities. This individual will play a key role in enabling AI-powered solutions through native platform functionality, delivering practical business outcomes, and ensuring compliance with enterprise governance and security standards.

The ideal candidate combines hands‑on experience with modern LLM platforms, strong software engineering fundamentals, and the ability to work directly with business partners to translate ideas into production‑ready solutions.

Key Responsibilities

• Design, deploy, and manage reusable AI capabilities within enterprise AI platforms, including skills, tools, and plug‑in functionality.
• Drive the complete lifecycle of AI enablement solutions, from intake and configuration through deployment, governance, ongoing support, and optimization.
• Configure foundation models such as Claude, Gemini, and similar technologies to support enterprise automation and business workflows.
• Deliver AI solutions from concept through production implementation, ensuring scalability, security, and operational readiness.
• Integrate existing enterprise systems, services, and approved tools using available connectivity frameworks and protocols.
• Collaborate with Security, Cloud, and Infrastructure teams to implement appropriate access controls, identity management practices, and credential governance.
• Ensure solutions comply with established AI governance standards, including auditability, monitoring, risk controls, and operational guardrails.
• Partner with engineering, automation, data, and business teams to identify opportunities and deliver impactful AI‑driven capabilities.

Required Experience AI Platform Expertise

• Proven experience developing, publishing, and managing Claude Skills or plug‑ins in a production environment.
• Demonstrated success deploying AI capabilities that are actively used by business stakeholders.
• Ability to contribute immediately with minimal onboarding and ramp‑up time.

Enterprise Delivery Experience

• Experience building and deploying technology solutions within regulated or highly governed enterprise environments.
• Strong understanding of security controls, compliance requirements, access management, and operational governance.
• Comfortable working within structured delivery processes and change‑control frameworks.

Business Partnership Skills

• Strong communication and stakeholder management abilities.
• Capable of translating business challenges into practical AI‑enabled solutions.
• Experience collaborating with both technical and non‑technical audiences.

Technical Qualifications

• 5+ years of experience in software engineering, automation engineering, or a related technical discipline.
• At least 2 years of experience designing and deploying production‑grade solutions powered by large language models.
• Strong proficiency in Python development and API‑based integrations.
• Experience with enterprise software integration patterns and distributed systems.
• Solid engineering practices, including testing, source control, observability, monitoring, and supportability.
• Hands‑on experience with modern LLM ecosystems, including prompt engineering, model configuration, tool integration, and function execution.
• Experience working within public cloud environments such as Azure, Google Cloud Platform, Vertex AI, or equivalent technologies.
• Ability to evaluate AI use cases pragmatically and determine when traditional engineering approaches may be more effective.
• Self‑directed and capable of independently leading technical initiatives in a fast‑moving environment.

Preferred Qualifications

• Experience building AI agents and multi‑agent workflows.
• Familiarity with orchestration platforms and agent frameworks.
• Understanding of Model Context Protocol (MCP) implementations and agent‑to‑agent integrations.
• Experience with RAG architectures, vector databases, embeddings, and semantic search solutions.
• Knowledge of modern identity and access management concepts, including RBAC, service accounts, agent identities, and least‑privilege models.
• Previous experience supporting organizations operating within highly regulated industries such as insurance, financial services, healthcare, or similar sectors.