The job is looking to solve the problem of developing and deploying full-stack AI/ML software applications, including the specification, architecting, and implementation of secure, scalable cloud-based software and infrastructure to support AI/ML use cases.
Requirements
- Software Engineering (Java, Python)
- Cloud-native technologies and development (Python/FastAPI, SQL, Redis)
- Cloud security frameworks and compliance requirements (e.g., NIST, DoD STIGs)
- Proficiency with infrastructure-as-code tools (Terraform, Ansible, CloudFormation) for controlled deployments.
- AI/MLOps - (Azure AI Studio; AWS Sagemaker, Kubeflow, etc)
- Familiarity with LLM APIs (OpenAI API, AWS Bedrock/boto)
- Experience with modern LLM integration methods and applicable tools (LlamaIndex/LangChain)
Responsibilities
- Strong software architecture and programming skills, with proficiency in Java and Python
- Cloud and Information Technology - including cloud-based architecture, database architecture, and data sharing protocols
- Cloud Infrastructure Engineering – design, deployment and management of secure, scalable cloud architectures across AWS and Azure environments
- Configuration of networking, storage and compute resources to support data-intensive workloads
- The design and implementation of end-to-end AI solution, data services, and APIs into production applications
- Infrastructure-as-Code (IaC) using Terraform
- Expertise with Artificial Intelligence, Machine Learning, and Deep Learning for Generative AI and Predictive AI
Other
- Must be able to obtain/maintain Secret Clearance
- Occasionally, customer facing demonstrations of software technology are required.
- The candidate should be capable to work independently as a contributor to an agile, full stack AI/ML software development team.
- The candidate should also demonstrate a general understanding of or interest in gaining expertise in: Systems Engineering processes, methods, and tools as applied to systems lifecycles, Digital Engineering methodologies and tooling
