AI Solutions Engineer

Quick Facts

• Focus: infrastructure, integrations, observability, and developer tools to operationalize AI
• Work: LLM and RAG systems, vector search, monitoring/logging, internal developer interfaces
• Environment: AWS-based AI workloads and production support

Description

Design, deploy, and operate reliable infrastructure for ML, LLM, and RAG workloads, with a focus on hosting models and optimizing them in AWS. Build and maintain RAG systems (including Qdrant), create logging/monitoring/alerting to ensure health, performance, cost, and reliability, and provide AI services via APIs, plugins, SDKs, and CLI tools. Evaluate and integrate COTS, third-party, and open-source solutions, collaborating across engineering, data, infrastructure, and security teams.

Role Description

• Subject matter expert for MLOps and AI workload operations (deployment, inference services, resource utilization, production support)
• Integrate and support AI capabilities from COTS/third-party/open-source and internal applications
• Collaborate with stakeholders to deliver practical, maintainable AI integrations and workflows

Responsibilities

• Design, deploy, and operate observable infrastructure for ML/LLM/RAG workloads in AWS
• Host, maintain, monitor, and optimize RAG systems and retrieval infrastructure (e.g., Qdrant)
• Develop logging, observability, and alerting for system health, performance, cost, and reliability
• Deploy and operate model inference services and support production operations
• Integrate COTS products, third-party services, open-source tools, and custom AI capabilities with monitoring/security/cost/performance ownership
• Develop APIs, plugins, and reusable integrations (including OpenWebUI, Hugging Face, Wilbur, and other platforms)
• Build developer tools such as SDKs and command-line utilities for internal users
• Support frontend/back-end integration and AI service interfaces (e.g., Streamlit, NGINX load balancers, web infrastructure)
• Evaluate AI technologies and deliver user-focused solutions with performance, latency, scalability, reliability, and cost in mind

Requirements

• Experience designing, deploying, and operating reliable, observable software or ML systems in production
• Experience with ML workloads, cloud infrastructure, and model deployment (preferably AWS)
• Experience with one or more programming languages (Python, Rust, C, MATLAB, or equivalent)
• Experience developing and integrating APIs/services/plugins or other software interfaces
• Experience with containerization, deployment, and orchestration (Docker, Kubernetes, Dagster, or equivalent)
• Experience with logging, monitoring, troubleshooting, and production support
• Comfort with CLI tools and shell scripting (Bash or Windows batch)
• Knowledge of DevSecOps and CI/CD systems (Jenkins, GitLab, Bamboo, or equivalent)
• Experience integrating COTS products, third-party services, APIs, or open-source software into production
• Ability to collaborate with stakeholders to evaluate technical options and deliver practical solutions
• BS in CS/Engineering/Data Science (or related) or equivalent applicable experience
• 5–10 years professional experience in software engineering, cloud infrastructure, data engineering, MLOps, AI engineering, or related

Benefits

• Healthcare (medical, dental, vision, prescription drugs)
• Paid maternity and parental leave
• 50% company match per contributed dollar into 401(k) savings plan (up to $11,500)
• Tuition reimbursement
• Potential to operate company vehicles as part of assigned duties