Responsibilities
- Design and develop AI-enabled services and applications using Python and modern cloud-native architectures.
- Integrate large language models (LLMs) and AI APIs into production systems to enable intelligent automation and decision support.
- Build scalable microservices and APIs that support AI-driven workflows and data processing.
- Develop pipelines and services that process structured and unstructured data to support intelligent applications.
- Implement secure APIs and event-driven architectures that support data integration and AI-enabled services.
- Optimize AI-powered applications for performance, reliability, and scalability in cloud environments.
- Collaborate with engineers, product teams, and stakeholders to translate AI concepts into deployable solutions.
- Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.
Basic qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical discipline.
- 3–6 years of professional software engineering experience for mid-level candidates, or 6+ years for senior candidates, with hands-on experience developing or deploying AI-enabled applications.
- Strong programming skills in Python for developing AI-enabled applications, APIs, and microservices.
- Hands-on experience integrating Large Language Models (LLMs) or AI APIs (such as OpenAI, Bedrock, Claude, or similar) into production environments.
- Experience developing cloud-native applications using platforms such as AWS, Azure, or Google Cloud.
- Experience building REST APIs, microservices, and distributed systems that support scalable AI workflows.
- Familiarity with AI development frameworks and tools such as LangChain, Hugging Face, vector databases, or similar technologies.
- Experience working with modern development practices including Git, CI/CD pipelines, containerization, and DevOps workflows.
- Strong analytical and problem-solving abilities with the ability to troubleshoot complex systems.
- Effective communication and collaboration skills within cross-functional engineering teams.
- Strong attention to detail and commitment to building reliable, high-quality software solutions.
- Experience deploying AI solutions within regulated environments such as healthcare or government systems.
- Familiarity with FHIR, HL7, or healthcare interoperability standards.
- Experience with LLM orchestration frameworks or agent-based architecture.
- Experience with containerization technologies such as Docker or Kubernetes.
- Experience working with the Department of Veterans Affairs (VA).
- Ability to obtain a U.S. government clearance.
Benefits
- Medical/Dental/Vision
- 401k with Employer Match
- PTO + Federal Holidays
- Corporate Laptop
- Training Opportunities
Tags & focus areas
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