AI Solutions Engineer

Stratolaunch develops aerospace and defense technologies through reusable hypersonic flight testing. The AI Solutions Engineer builds and operates infrastructure, integrations, and developer tools to deploy AI capabilities across vehicle design, manufacturing, flight testing, and business teams.

Responsibilities:

• Design, deploy, and operate reliable infrastructure for ML, LLM, and RAG workloads, with a focus on running and optimizing models in AWS and supporting future AI compute requirements
• Host, maintain, monitor, and optimize RAG systems, including Qdrant and other retrieval infrastructure, while developing the logging, observability, and alerting needed to assess system health, performance, cost, and reliability
• Serve as a subject matter expert for MLOps and AI workload operations, including model deployment, inference services, resource utilization, and production support
• Integrate and support COTS products, third-party services, open-source tools, and custom AI capabilities, sharing responsibility for their monitoring, security, cost, performance, and reliability
• Develop APIs, plugins, and reusable integrations that make AI capabilities available to Stratolaunch teams, including tools for OpenWebUI, Hugging Face, Wilbur, and other applicable platforms
• Collaborate with internal stakeholders to develop team-specific AI integrations and workflows, and to deliver practical interfaces between AI services and existing applications, including frontend applications, Streamlit services, NGINX load balancers, and other web infrastructure
• Collaborate with software, data, infrastructure, security, and domain teams to evaluate AI technologies and deliver maintainable, user-focused solutions

Requirements:

• Experience designing, deploying, and operating reliable, observable software or ML systems in production
• Experience with ML workloads, cloud infrastructure, and model deployment, preferably in AWS
• Experience with one or more programming languages, such as Python, Rust, C, MATLAB, or equivalent
• Experience developing and integrating APIs, services, plugins, or other software interfaces
• Experience with containerization, deployment, and orchestration technologies such as Docker, Kubernetes, Dagster, or equivalent tools
• Experience with logging, monitoring, troubleshooting, and production support
• Comfort with CLI tools and shell scripting, such as Bash or Windows batch scripting
• Knowledge of DevSecOps and CI/CD systems, such as Jenkins, GitLab, Bamboo, or equivalent tools
• Experience integrating COTS products, third-party services, APIs, or open-source software into production environments
• Ability to collaborate with stakeholders to evaluate technical options and deliver practical solutions
• MLOps experience, including hosting, maintaining, monitoring, and optimizing LLM and RAG systems
• Experience running models and inference workloads in AWS, including services such as EC2, Lambda, ECS, EKS, S3, and CloudWatch
• Experience optimizing RAG systems, vector databases, and retrieval pipelines, including Qdrant
• Familiarity with LLM APIs, embedding models, Hugging Face tools or plugins, OpenWebUI, and related AI application frameworks
• Experience developing reusable plugins, SDKs, CLIs, or developer platforms for multiple teams
• Experience with frontend and backend integration, Streamlit, NGINX, reverse proxies, load balancing, or related web-service infrastructure
• Experience evaluating AI systems based on performance, latency, scalability, reliability, cost, and resource utilization
• Familiarity with relational, non-relational, and vector databases, such as PostgreSQL, MongoDB, and Qdrant
• BS in Computer Science, Engineering, Data Science, or a related field, or applicable experience in lieu of degree
• 5-10 years professional experience in software engineering, cloud infrastructure, data engineering, MLOps, AI engineering, or a related discipline
• Aerospace experience or familiarity with engineering, CAD, analytics, visualization, or flight-test workflows