AI Solutions Engineer

Stratolaunch • Mojave, CA

1d • $142,000 - $165,000 • Onsite

About The Position

Stratolaunch accelerates the development of next-generation aerospace and defense technologies through reusable hypersonic flight testing. Its autonomous, air-launched Talon-A vehicles provide access to operationally realistic flight environments at speeds exceeding Mach 5, helping customers validate system performance and advance technologies toward deployment.

The AI Solutions Engineer contributes to developing, integrating, and operating AI capabilities across Stratolaunch. Within the Enterprise Software Team, the role helps integrate commercial off-the-shelf (COTS) and custom tools so users can access the right data, functionality, and AI-enabled capabilities. The position focuses on infrastructure, integrations, observability, and developer tools that operationalize AI for vehicle design, manufacturing, flight test, and business teams. Work may include operating LLM and RAG systems, developing logging and monitoring capabilities, running AI workloads in AWS, optimizing vector search systems such as Qdrant, and building APIs, plugins, SDKs, and command-line tools.

The position also evaluates and integrates COTS solutions, third-party services, open-source tools, and internally developed applications. Potential applications include meeting summarization and scheduling, CAD integrations, OpenWebUI and Hugging Face tools, team-specific plugins, and reusable software development kits. The scope may be tailored to the selected candidate’s interests and experience, with particular opportunity for candidates experienced in ML workload optimization and cloud-based model operations.

The position is located in Mojave, California. No travel is required. The work schedule is a 9/80 schedule. The role may involve operating company vehicles as part of assigned duties. Base salary is $142,000–$165,000; salary and leveling are established case by case based on factors including location, education, skills, and experience.

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.
• A bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or applicable experience in lieu of a degree.
• 5–10 years of professional experience in software engineering, cloud infrastructure, data engineering, MLOps, AI engineering, or a related discipline.

Nice To Haves

• 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.
• Aerospace experience or familiarity with engineering, CAD, analytics, visualization, or flight-test workflows.

Responsibilities

• Design, deploy, and operate reliable infrastructure for ML, LLM, and RAG workloads, focusing 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, and develop logging, observability, and alerting 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 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 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.

Benefits

• Healthcare coverage, including medical, dental, vision, and prescription drugs.
• Paid maternity and parental leave.
• Company match of 50% per contributed dollar into the 401(k) savings plan, up to $11,500.
• Tuition reimbursement.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level