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MLO Engineer (Machine Learning Operations)
Mojave, California, United States
Actively hiring, first seen by us 3 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
Before you apply
- Work authorization
- No visa sponsorship
What you’ll work on
Full postingDesign, deploy, and operate infrastructure for ML, LLM, and RAG workloads in AWS
Host, monitor, and optimize RAG systems including Qdrant, with logging and alerting
Serve as subject matter expert for model deployment, inference, and production support
Develop APIs, plugins, and reusable integrations for OpenWebUI, Hugging Face, and other platforms
Integrate and support COTS, third-party, and open-source AI tools, including their security and cost
From the employer’s posting
Design, deploy, and operate reliable infrastructure for ML, LLM, and RAG workloads, with a focus on running and optimizing models in AWS
Host, maintain, monitor, and optimize RAG systems, including Qdrant and other retrieval infrastructure
Serve as a subject matter expert for MLOps and AI workload operations, including model deployment, inference services, resource utilization, and production support
Develop APIs, plugins, and reusable integrations that make AI capabilities available to Stratolaunch teams
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
What you’ll bring
All qualificationsCore experience
- Experience designing, deploying, and operating reliable, observable software or ML systems in production.
- Experience running models and inference workloads in AWS, including services such as EC2, Lambda, ECS, EKS, S3, and CloudWatch.
- 5-10 years professional experience in software engineering, cloud infrastructure, data engineering, MLOps, AI engineering, or a related discipline.
- Experience with ML workloads, cloud infrastructure, and model deployment, preferably in AWS.
- Experience optimizing RAG systems, vector databases, and retrieval pipelines, including Qdrant.
- Experience with one or more programming languages, such as Python, Rust, C, MATLAB, or equivalent.
Qualification wording
Experience designing, deploying, and operating reliable, observable software or ML systems in production.
Experience running models and inference workloads in AWS, including services such as EC2, Lambda, ECS, EKS, S3, and CloudWatch.
5-10 years professional experience in software engineering, cloud infrastructure, data engineering, MLOps, AI engineering, or a related discipline.
Experience with ML workloads, cloud infrastructure, and model deployment, preferably in AWS.
Experience optimizing RAG systems, vector databases, and retrieval pipelines, including Qdrant.
Experience with one or more programming languages, such as Python, Rust, C, MATLAB, or equivalent.
Education & alternatives
EDUCATION & EXPERIENCE - 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.
Posting record
Report an errorActively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 10, 20263 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Stratolaunch is hiring
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Bash
- C
- MATLAB
- Python
- Rust
- Shell
- AWS
- Docker
- Kubernetes
- MongoDB
- PostgreSQL
- S3
- Dagster
- Hugging Face
- Streamlit
Where the posting names them
- 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 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.
The AI Solutions Engineer contributes to the development, integration, and operation of AI capabilities across Stratolaunch. The Enterprise Software Team integrates a mix of COTS and custom tools to provide the right data, functionality, and AI-enabled capabilities to the right user at the right time. This position will focus on the infrastructure, integrations, observability, and developer tools required to operationalize AI for vehicle design, manufacture, flight test, and business teams. Responsibilities may include hosting and maintaining large language model (LLM) and Retrieval-Augmented Generation (RAG) systems; developing logging and monitoring capabilities; operating AI workloads in AWS; optimizing vector search systems such as Qdrant; and building APIs, plugins, SDKs, and command-line tools for internal users. The AI Platform and MLOps Engineer will also evaluate and integrate 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 of this position 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.
ESSENTIAL 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.
- 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.
- 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.
- 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.
- 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.
This position will focus on the infrastructure, integrations, observability, and developer tools required to operationalize AI for vehicle design, manufacture, flight test, and business teams. Responsibilities may include hosting and maintaining large language model (LLM) and Retrieval-Augmented Generation (RAG) systems; developing logging and monitoring capabilities; operating AI workloads in AWS; optimizing vector search systems such as Qdrant; and building APIs, plugins, SDKs, and command-line tools for internal users. The AI Platform and MLOps Engineer will also evaluate and integrate 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 of this position 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. ESSENTIAL RESPONSIBILITIES
- 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.
- 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.
- 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.
- 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.
Your next step
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Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
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About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
Stratolaunch accelerates the development of next-generation aerospace and defense technologies through reusable hypersonic flight testing. Our autonomous, air-launched Talon-A vehicles provide access to operationally realistic flight environments at speeds exceeding Mach 5, delivering critical data that helps customers validate system performance and advance technologies toward deployment. Our team brings together expertise in engineering, manufacturing, and flight operations to solve complex challenges and advance capabilities essential to national security.
The AI Solutions Engineer contributes to the development, integration, and operation of AI capabilities across Stratolaunch. The Enterprise Software Team integrates a mix of COTS and custom tools to provide the right data, functionality, and AI-enabled capabilities to the right user at the right time.
This position will focus on the infrastructure, integrations, observability, and developer tools required to operationalize AI for vehicle design, manufacture, flight test, and business teams. Responsibilities may include hosting and maintaining large language model (LLM) and Retrieval-Augmented Generation (RAG) systems; developing logging and monitoring capabilities; operating AI workloads in AWS; optimizing vector search systems such as Qdrant; and building APIs, plugins, SDKs, and command-line tools for internal users.
The AI Platform and MLOps Engineer will also evaluate and integrate 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 of this position 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.
ESSENTIAL 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.
QUALIFICATIONS
Required
- 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.
Preferred
- 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.
EDUCATION & EXPERIENCE
- 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.
- $142,000-$165,000
Special remarks regarding work environment, if applicable
- This position is located in Mojave, CA
- No travel required
- 9/80 Schedule
Base salary and leveling are established on a case-by-case basis. We consider several factors to ensure a competitive offer, including your location, educational background, and a comprehensive assessment of the skills and experience demonstrated during your interviews.
Benefits and our location(s):
- 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
This position may involve the operation of company vehicles as part of assigned duties.
Mojave Air & Spaceport is located just outside of Lancaster/Palmdale and is only 95 miles north of Los Angeles. The area offers both affordability and central access to a variety of California's best attractions and landmarks.
Not much for the outdoors and prefer the city life? You’re within a half day’s drive or less of Los Angeles, San Diego, Las Vegas, and San Francisco where you can find a variety of live entertainment, museums, tours, and historical sight-seeing. Theme parks are in abundance, with Six Flags Magic Mountain, Disneyland, and Knotts Berry Farm ready to meet the needs of rollercoaster thrill seekers.
Food and drink fanatics are in a haven of delectable choices. Wine aficionados will also find easy access to some of the state’s best vino in Paso Robles, Santa Ynez, and even more locally in Tehachapi and Agua Dulce. Beer fanatics will find some of the best West Coast IPAs at Bravery and Lucky Luke’s in Lancaster. Any food option you crave is covered, and we always recommend the authentic and fresh Mexican food that you won’t find anywhere else in the United States.
Stratolaunch is an equal opportunity employer that values diversity at our company. We encourage people from all walks of life to apply, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other characteristic protected by law. Our management team does not tolerate discrimination or harassment at any point during our employees’ and applicants’ time with us, especially with respect to recruitment, hiring, placement, promotion, transfer, training, compensation, benefits, employee activities, and general treatment during employment. For purposes of transparency, U.S. Citizenship may be required for some positions with a government clearance and certain other restricted positions.
Equal Employment Opportunity is the Law :
https://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf
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