AI Engineer

We’re seeking a highly technical and hands-on AI Engineer to join our growing team. This role will work across internal AI tooling, our internal AI platform, and AI capabilities within existing products, helping bring practical AI solutions into production across the organization.

You’ll contribute to a range of applied AI initiatives, from improving existing products and workflows to developing new AI-powered capabilities where appropriate. The scope may include prompt and context engineering, AI security, AI DevOps and hosting, evaluation frameworks, harnesses, benchmarks, and production deployment.

We’re looking for someone who is deeply technical, passionate about AI development, and eager to grow with us as we scale.

Responsibilities:

• Develop and improve internal AI tooling, platform capabilities, and AI-enabled product functionality, including integrating AI capabilities and APIs into existing products as needed
• Build LLM-powered solutions using prompt engineering, context engineering, RAG, agentic workflows, and generative AI
• Create and maintain AI harnesses, evals, benchmarks, and testing frameworks to measure quality, reliability, and performance
• Apply ML algorithms such as linear regression, logistic regression, tree-based methods, and neural networks where applicable
• Contribute to secure, scalable AI architecture, infrastructure, DevOps, hosting, and system optimization leveraging AWS services
• Work cross-functionally with product, data engineering, and software engineering teams to align AI initiatives with business needs
• Stay current with emerging AI technologies, contribute to engineering best practices, and mentor junior engineers as the team grows

Requirements:

• A Bachelor’s degree in Computer Science, Mathematics, Physics, or a related STEM field
• 3+ years of backend software development experience, with a strong AI/ML focus
• Experience with generative AI technologies, including prompt engineering and API integrations
• A solid understanding of ML algorithms (regression models, tree-based algorithms, neural networks)
• Proficiency with AWS infrastructure (EC2, S3, networking fundamentals)
• A strong foundation in general computer engineering and networking principles

Nice-to-Have:

• Experience with AI research projects (e.g., public health AI applications, LLM fine-tuning)
• A background in computer architecture (CUDA, GPU parallelization, compute optimization)
• Familiarity with healthcare data is a plus, but not required
• Experience working in high-growth tech companies or research labs