AI Solutions Engineer

Health Data Innovations (HDI)

. Find business processes where AI can help and determine whether an assistant, AI-assisted workflow, or agent is appropriate .

Posted 11/4/2026full-timeCleveland • Ohio • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI-assisted workflows and agents, with strong skills in Python scripting, cloud AI platforms, and infrastructure management. Proficient in integrating APIs and databases while ensuring compliance and security in AI solutions.

Highest-signal resume keywords

AI Workflow DevelopmentPython ScriptingCloud AI PlatformsLinux AdministrationAPI Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonAI AgentsLLM APIsMCP ServersDatabase IntegrationScriptingAutomationNetworking FundamentalsSecurity FundamentalsCompliance Frameworks

Soft Skills

Independent WorkPriority ManagementUser Support

Tools & Technologies

Amazon BedrockMicrosoft 365DockerGitAzure OpenAILinux ServersWindows ServerTerraformAnsibleOpenTelemetry

Industry Keywords

HITRUSTHIPAASOC 2ISO 27001Least PrivilegeHuman-in-the-LoopAudit LoggingVirtualizationGPU HostingRemote Desktop Services

Tech Stack

Tools & technologies

AnsibleAWSAzureCloudDockerFirewallsGrafanaLinuxMySQLPackerPostgresPrometheusPythonTerraformTypeScriptVMwareGo

About the role

Key responsibilities & impact

Find business processes where AI can help and determine whether an assistant, AI-assisted workflow, or agent is appropriate
Build and improve AI-assisted workflows, skills, and plugins
Design, build, and deploy AI agents and multi-step workflows using tool use/function calling, prompt and context design, and agent frameworks
Build and support Model Context Protocol servers, database bridges, and Microsoft 365 integrations
Test and evaluate AI output for accuracy and reliability and identify regressions
Implement guardrails, least-privilege permissions, human-in-the-loop approvals, and audit logging
Own AI solutions in production by monitoring behavior, usage, and cost; troubleshooting; and versioning, maintaining, and retiring solutions
Write Python scripts and code managed through Git and pull-request review
Track new models, agent frameworks, and vendor releases; run proofs of concept; and recommend technologies
Support infrastructure for AI services, including Amazon Bedrock, Entra ID/SSO, Linux and Windows servers, VMs, and GPU hosts
Write documentation, runbooks, and guides suitable for compliance audits
Work directly with executives and business functions to shape AI adoption and roadmap

Requirements

What you’ll need

3+ years in IT infrastructure, systems administration, DevOps, or software development
Hands-on experience building with LLMs or AI agents, including tool use, MCP, or multi-step workflows
Experience with LLM APIs or cloud AI platforms such as Amazon Bedrock, Azure OpenAI, or Vertex AI
Solid Linux skills with RHEL or Ubuntu
Working knowledge of Windows Server and Docker
Networking and security fundamentals, including firewalls, segmentation, and least privilege
Scripting and automation skills; Python preferred, with PowerShell or Bash a plus
Experience integrating with APIs, databases, and business systems
Familiarity with Git workflows and code review
Ability to turn experiments into reliable, documented systems
Ability to work independently, manage priorities, and support non-technical users in a small-team environment
Preferred: Building MCP servers or using agent frameworks
Preferred: Experience taking an agent from prototype to production
Preferred: Experience testing and evaluating LLM output
Preferred: Software development experience in Python, Go, or TypeScript
Preferred: RAG pipelines and vector databases, including pgvector, Qdrant, Chroma, or Milvus
Preferred: Experience with Entra ID / Azure AD, SSO, and OAuth 2.0 / OIDC
Preferred: Experience under compliance frameworks such as HITRUST, HIPAA, SOC 2, or ISO 27001
Preferred: AWS IAM, cost management, and Bedrock guardrails
Preferred: Running open-weight models with vLLM or llama.cpp on NVIDIA GPUs, including vGPU or GPU passthrough
Preferred: Remote Desktop Services, Azure Arc, and virtualization with VMware, Proxmox, or Hyper-V
Preferred: OpenTelemetry, Prometheus, or Grafana
Preferred: MySQL Server or PostgreSQL administration
Preferred: Microsoft Graph API
Preferred: Terraform, Ansible, or Packer

Benefits

Comp & perks

Hybrid work arrangement
Periodic in-office presence in Cleveland
Team meetings and onboarding/training sessions
Scheduled collaboration with stakeholders during planning and rollout phases