As a Software Engineer - AI Workflows at Flume Health, your primary responsibilities will include architecting AI workflows to generate and refine data mappings using LLM APIs like OpenAI and Google. You will implement validation feedback loops, develop prompt engineering strategies, and orchestrate workflows with tools like Python and Airflow. Collaboration with other teams is essential to ensure these workflows integrate smoothly into our core systems, prioritizing performance and compliance on our journey to improve healthcare access.

To be considered for the Software Engineer - AI Workflows role at Flume Health, you need at least 3 years of software engineering experience, especially in data, ML, or AI product development areas. Proven experience with LLM APIs, proficiency in Python, and familiarity with orchestration tools like Airflow are also essential. Additionally, a strong understanding of cloud-native environments, as well as effective communication skills, will greatly benefit you in this position.

As a Software Engineer - AI Workflows at Flume Health, you will engage with a variety of cutting-edge technologies. You'll utilize LLM tools and APIs such as OpenAI and Vertex AI, and work with Python and Airflow for workflow orchestration. Additionally, you'll leverage cloud infrastructure on platforms like GCP or AWS, using Docker and Kubernetes. Familiarity with healthcare standards like FHIR and HIPAA-compliant pipelines will be beneficial, as will exposure to monitoring and versioning technologies.

Yes, there is a travel requirement for the Software Engineer - AI Workflows role at Flume Health. Candidates will need to participate in in-person team meetings once a quarter. This is a great opportunity to connect with the team and foster collaboration, ensuring everyone is aligned on our goals.

Flume Health offers a variety of attractive perks and benefits for the Software Engineer - AI Workflows position, including flexible PTO, a robust stock option plan, and a WFH stipend, recognizing our remote-first culture. You’ll also enjoy competitive compensation, comprehensive health coverage (medical, dental, vision), and a 401k plan with a 4% match, all designed to support your well-being and financial health.

In my previous roles, I've worked with various LLM APIs, specifically focusing on their integration into data workflows. I utilized tools like OpenAI’s API to generate data mappings and employed techniques like prompt chaining for refinement. My experiences taught me about trade-offs between cost and latency, which I handled by optimizing the workflow architecture to balance performance with budget.

When approaching prompt engineering, I start by identifying the user’s specific requirements and then iteratively refine the prompts. I analyze output quality and use structured validation techniques to streamline the process. By maintaining flexibility in prompt design and integrating feedback loops, I ensure that AI outputs align with the precise needs of the project.

In a recent project, I led the integration of multiple data sources for a health application, which involved complex workflows. I developed a comprehensive pipeline using Airflow, which included automated validation checks and real-time error handling. By collaborating with cross-functional teams, I ensured smooth data integration, which improved data accessibility and stakeholders’ decision-making processes.

I have utilized Airflow extensively for orchestration, given its flexibility and ease of use for modeling complex workflows. I’ve also explored Dagster and Prefect for certain projects. These tools helped me create reliable scheduling of jobs and integrated quality checks to ensure output consistency.

I regularly engage with various AI and ML communities through webinars, workshops, and online courses. I also follow industry news and contribute to open-source projects, which keeps my knowledge up-to-date and exposes me to cutting-edge tools and methodologies that I can apply in my work.

I have significant experience working in both GCP and AWS cloud-native environments. Most recently, I utilized GCP infrastructure for deploying applications, leveraging services like BigQuery for data warehousing and Kubernetes for container orchestration. My experience extends to using Docker for developing and deploying microservices within these environments.

In an AI workflow, I implement structured validation checks that gather user and system feedback. Once feedback is received, I analyze the responses to make necessary adjustments to the prompts or workflow parameters. This iterative process not only enhances the quality of the outputs but also builds trust with stakeholders who see their input directly influencing the results.

To ensure compliance in my workflows, I integrate tasks that adhere to healthcare regulations like HIPAA from the ground up. I create pipelines that include audits and validations, ensuring that all data handling processes meet compliance requirements. This also involves regular collaboration with compliance teams to stay updated on the latest regulations affecting workflow design.

I implement several strategies to boost AI output accuracy, such as using iterative refinement techniques like re-running with adjusted prompts based on validation results. Additionally, I utilize feedback loops that allow the AI model to learn from its mistakes. Incorporating domain-specific rules also helps enhance the relevance and accuracy of the outputs.

I’m excited about the opportunity to work at Flume Health because of its commitment to transforming healthcare through technology. I believe my background in AI and data integration aligns perfectly with your mission to enhance healthcare access. Contributing to streamlined health plan administration is a cause I am passionate about, and I’m eager to leverage my expertise to make a positive impact.