Software Engineer

Seeking an AI-forward Senior Software Engineer to build secure, reliable, and scalable

AI-enabled products and capabilities.

The ideal candidate combines strong software engineering fundamentals with hands-on

experience applying generative AI to meaningful business and customer problems. They will

partner across Engineering, Product, Data, Security, and Infrastructure to turn emerging AI

capabilities into durable production solutions.

Responsibilities

Design and build production-grade AI agent systems, including multi-agent workflows,

orchestration layers, and the infrastructure needed to make them reliable, observable,

and auditable.

Design retrieval, context, and memory systems that ground AI outputs and support

reliable multi-step workflows.

Develop AI-powered decision-support systems that meet the accuracy, traceability, and

explainability requirements of healthcare and other regulated environments.

Develop distributed backend services, event-driven workers, job pipelines, and

multi-stage processing systems that deliver AI outputs reliably under load.

Build intuitive, product-quality user experiences using React and TypeScript that make

complex AI capabilities understandable and actionable.

Establish patterns for AI quality assurance, including automated evaluations, regression

testing, groundedness checks, and compliance guardrails.

Own the deployment and operation of AI-enabled systems through CI/CD, cloud

infrastructure, monitoring, alerting, and production support.

Evaluate models, frameworks, and architectural approaches based on accuracy, latency,

reliability, security, and cost.

Partner with Product, Engineering, Data, Security, and Infrastructure to deliver

responsible and scalable AI solutions.

Mentor engineers and help raise engineering standards across the team.

Five or more years of software engineering experience, including ownership of complex

systems from design through production.

Hands-on experience building AI-enabled applications, including agentic workflows, LLM

integrations, evaluation systems, and production AI pipelines.

Strong Python expertise, including asynchronous services, type-safe application

development, API design, testing, and scalable backend systems.

Strong React and TypeScript expertise, with experience building polished, reliable, and

maintainable product experiences.

Experience with RAG, embeddings, vector search, context engineering, or other

retrieval-based AI architectures.

Understanding of security, privacy, access control, and data-governance considerations

for AI systems.

Strong distributed systems fundamentals, including event-driven architectures, message

queues, background workers, job pipelines, and state management.

Fluency with relational databases and strong data-modeling judgment.

Strong judgment around how AI systems should be tested, evaluated, monitored, and

improved in production.