The Role
You'll build and scale the core infrastructure that powers Bluenote's AI platform for life sciences companies. You'll design robust, secure systems that enable our clients to deploy AI-powered workflows at enterprise scale, helping bring life-saving therapies to patients faster.
This role is ideal for technically strong engineers who are passionate about building foundational systems and excited to be at the forefront of AI infrastructure for real-world applications in life sciences.
This is a great opportunity for those who aspire to be technical leaders and want to shape the future of AI infrastructure.
What You’ll Do
Build and maintain Bluenote's core platform infrastructure, including APIs, data pipelines, RAG and agent orchestrator.
Design scalable architectures that support enterprise-grade AI workflows and agent orchestration.
Develop tooling and frameworks that enable rapid development and deployment of AI solutions.
Implement robust monitoring, logging, and observability systems to ensure platform reliability.
Collaborate with the Agent Engineering team to understand platform requirements and optimize for their use cases.
Own platform security, performance, and scalability as we grow our customer base.
Who You Are
Systems-oriented with a passion for building reliable, maintainable infrastructure.
Comfortable navigating ambiguity and rapidly evolving technical requirements.
Curious about how AI infrastructure can transform the life sciences industry.
Exhibits thoroughness and precision in system design and implementation.
What You Bring
8+ years of post-college experience in software engineering, platform engineering, or related field.
Proficiency in Python, or similar programming languages.
Experience building and scaling distributed systems, APIs, and cloud infrastructure.
Hands-on experience with containerization, orchestration, and CI/CD pipelines.
Understanding of database design, and performance optimization.
Experience with cloud platforms (AWS, GCP, Azure) and infrastructure as code.
Prior experience with ML/AI infrastructure, LLMOps, or data engineering is a strong plus.
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