2 months ago
Dayton, OH, USAMid Level
Responsibilities
- Build and evaluate machine learning, deep learning, and generative AI solutions for mission use cases.
- Develop and integrate AI/ML capabilities across software applications, APIs, services, data pipelines, and existing technical environments.
- Assess models and datasets for suitability, availability, access requirements, technical feasibility, and mission value.
- Enable LLM and GenAI capabilities through repeatable workflows, interfaces, services, and approved access mechanisms.
- Prepare and curate datasets for model training, evaluation, testing, and operational AI workflows.
- Translate prototypes into usable capabilities by addressing performance, scalability, reproducibility, integration, and deployment.
- Develop interfaces and services for applications and users to interact with AI/ML capabilities and model or provider metadata.
- Partner with software engineers, data engineers, technical leadership, product stakeholders, subject-matter experts, and mission users.
- Create technical documentation, implementation guidance, runbooks, and repeatable processes for adoption and sustainment.
- Evaluate deployed capabilities and use technical and user feedback to improve performance and mission utility.
Requirements
- Strong hands-on foundation in applied machine learning or AI engineering beyond theoretical exercises or isolated notebooks.
- Software development ability, preferably with Python, and experience with a major ML framework such as PyTorch or TensorFlow.
- Experience in one or more applied AI/ML areas, such as computer vision, natural language processing, generative AI and large language models, predictive modeling, object detection or classification, or modeling and simulation.
- Ability to explain personally owned work, technical decisions, evaluation methods, and how capabilities reached users.
- Comfort working with datasets for model development and evaluation and collaborating across software, data, systems, and mission teams.
- Active TS/SCI clearance required.
- Preferred experience includes Hugging Face, Ollama, APIs, microservices, Docker, cloud-native deployment, AWS, Azure, DevSecOps, MLOps, model or data versioning, metadata management, production AI/ML pipelines, sensitive or regulated datasets, and federal, DoD, or Intelligence Community environments.
Benefits
- Company-supported certifications aligned with current and future program work.
- Advancement and leadership opportunities.
- 401(k) with 100% match up to 6%.
- Medical, dental, vision, life, and disability coverage.
- Generous paid time off and paid holidays.
- Home-office equipment and remote-work support where applicable.
- Dayton, Ohio work location with onsite and hybrid expectations still being finalized; candidates should be prepared to discuss supporting work in Dayton if required.
About Rackner
Rackner is a cloud-native software consultancy that delivers DevSecOps, AI/ML, Kubernetes, and edge-computing solutions for startups, enterprises, and U.S. federal agencies across public and private clouds. Founded in 2015 and headquartered in Silver Spring, Maryland, it is privately held and a Kubernetes Certified Service Provider and CNCF member, offering services from cloud and hybrid-cloud development to web/mobile applications supporting mission-critical systems.