GRVTY
AI/MLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“AI/ML Engineer at GRVTY. Skills: AI/ML, RAG pipelines, LLM integration. Implement RAG pipelines. Maintain RAG pipelines”
Industry & Context.
Advanced analytical problem-solving
TS/SCI + Polygraph clearance, Work onsite 40 hours
What They're Looking For.
Must Have
Active TS/SCI with Polygraph Clearance, 8 years of experience with a Bachelor’s, 7 years of experience with a Masters, 6 years of experience with a Doctorate, Ability to conduct independent technical research, Ability to evaluate emerging AI/ML approaches, Ability to apply advanced analytical problem-solving, Ability to rapidly learn and apply new AI/ML methodologies, Experience developing AI/ML applications, Proficiency in Python, Proficiency in modern AI/ML libraries, Proficiency in AI/ML frameworks, Proficiency in AI/ML API integrations, Work onsite full 40 hours, Cover Sponsor's core office hours
Nice to Have
Advanced research experience in machine learning, Advanced research experience in deep learning, Advanced research experience in natural language processing, Advanced research experience in generative AI, Advanced research experience in reinforcement learning, Advanced research experience in computer vision, Experience publishing research, Experience contributing to open-source AI/ML initiatives, Experience leading experimental development efforts, Experience leading prototype development efforts, Familiarity with model evaluation frameworks, Familiarity with fine-tuning workflows, Familiarity with inference optimization, Familiarity with AI observability tools, Familiarity with AI monitoring tools, Experience with vector databases, Experience with AWS, Experience with cloud environments, Experience with Docker, Experience with containerized AI/ML development workflows, Experience designing REST APIs, Experience integrating REST APIs, Experience designing scalable data architectures, Experience integrating scalable data architectures
What You'll Do.
Implement RAG pipelines
Maintain RAG pipelines
Integrate LLMs into applications
Develop REST API interactions
Maintain REST API interactions
Design Postgres schemas
Refine Postgres schemas
How You'll Work.
Team & Collaboration
Intelligence analyst groups; Mission organizations; Mission partner organizations
Full Job Description
What Impact You'll Have GRVTY’s team provides tactical data engineering solutions. We embed skilled Data Engineers, Data Scientists, and ETL Developers directly into intelligence analyst groups to be their go-to data wranglers. We develop new tools, code, and services to execute data engineering activities. Our engineers work to collect, process, and feed analytic tools, turning data into intelligence in response to immediate mission needs, with direct impact on real world situations. You will see your work used here on a daily basis, and you'll have the opportunity to support a variety of Sponsor mission organizations and mission partner organizations. This is a time of development and growth on the program, with an increasing number of missions being supported. The work is high impact and important, and the customer moves quickly. The environment is fast-paced, flexible, and open to innovation - you'll have more latitude here in choosing how to achieve results than on many other projects. The customer cares more about what you can do as opposed to your years of experience, and work hours are typically quite flexible - roll up your sleeves, get things done, and no one cares much about the specific hours that you work. The work space itself is also quite nice, and there is an excellent cafeteria! Work on this program takes place in McLean, VA and in various field offices throughout Northern VA (we cannot support remote work) and requires a TS/SCI + Polygraph clearance (acceptable to this customer). What You'll be Owning GRVTY is seeking an experienced AI/ML Engineer with a TS/SCI + Poly clearance (applicable to this customer) to join one of our top projects in McLean, VA. Implementing and maintaining RAG pipelines, including document processing, embedding generation, retrieval configuration, and prompt assembly Integrating LLMs into applications using available APIs and frameworks Developing and maintaining REST API interactions to support data retrieval and system
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