United States onlyThis position is currently remote; however, in accordance with federal contract requirements and company policy, there may be future changes to work location expectations. Candidates should be prepared for the possibility of a return to the office, either partially or fully, based on client directives, contractual obligations, or company policies. Any such changes will be communicated in advance.
Must be a U.S. Citizen with the ability to obtain and maintain a government suitability clearance.
Role Overview
As an AI Software Engineer you will translate emerging research into production ready, Generative AI capabilities that improve the way federal agencies serve the public. You will own the full lifecycle—from ideation and prototyping through cloud deployment and MLOps—while working in a small, highly collaborative team of engineers, data scientists, and domain experts.
Key Responsibilities
- Research, design, and develop production grade Generative AI and intelligent automation solutions using LLMs, RAG pipelines, and vector databases.
- Lead end to end development: data collection/cleansing, model finetuning, evaluation, optimization, containerization, and secure cloud deployment (Azure/AWS).
- Implement LLMOps/MLOps pipelines for automated testing, bias detection, monitoring, and rollback.
- Apply Responsible AI principles and federal compliance standards (e.g., NIST RMF, FedRAMP) throughout the solution lifecycle.
- Translate vague mission requirements into technical designs; iterate quickly with stakeholders via agile ceremonies and rapid prototyping.
- Contribute to the VITAL Innovation Lab roadmap by identifying research topics, publishing internal white papers, and presenting at communities of practice.
- Education: Bachelor’s Degree in Computer Science, Computer Engineering, Data Science, Computational Linguistics, or a related field; Master’s is a bonus.
- Experience: 1-3+ years of professional experience building and shipping ML or NLP systems, including focus on LLM based or Generative AI solutions.
- Technical skills:
- Expert Python and strong software engineering fundamentals (data structures, algorithms, testing, CI/CD, Git, containers).
- Hands on with modern ML/LLM tooling (PyTorch or TensorFlow; LangChain, LangGraph, vector databases such as Pinecone, Azure AI Search, FAISS) and AI Agents.
- Proven experience deploying workloads to Azure (preferred), AWS, or another major cloud provider.
- Familiarity with DevOps/Infrastructure as Code (GitHub Actions, Terraform/Bicep, Docker/Kubernetes).
- Strong written and verbal communication; ability to explain complex concepts to nontechnical audiences.
Bonus: Aspiration to develop leadership and team management skills.
