Job Title:
Staff Software Engineer
Job Type:
Contract
Contract Length:
6–12 months (with strong potential for extension)
Rate:
Competitive hourly rate (USD) — based on experience
Role Location:
Remote (LATAM) — must be able to work PST hours
A fast-growing AI technology company building backend systems that enable clinical and operational tools used by healthcare organizations. The team combines engineering and research expertise to build safe, reliable AI services for use in regulated data environments. The company is focused on practical, production-grade systems rather than research prototypes.
Design and build scalable backend infrastructure to support AI applications used in healthcare environments.
Collaborate with ML engineers and scientists to develop high-throughput data pipelines and microservices.
Develop APIs and distributed systems that enable secure data retrieval, processing, and model integration.
Ensure patient data protection through strong privacy and security measures across systems.
Monitor, optimize, and maintain backend performance to meet enterprise reliability standards.
Partner with product managers and healthcare teams to translate clinical needs into technical solutions.
Mentor junior engineers, fostering technical growth and best practices within the engineering team.
Must Have:
7+ years of experience in backend engineering (Python, Go, or Java).
Proven experience building distributed systems (databases, pub/sub, Kafka, Redis).
Expertise in cloud platforms (GCP or Azure preferred; AWS acceptable).
Proficiency with Docker, Kubernetes, CI/CD, and cloud networking.
Strong understanding of SaaS architecture, data modeling, and security/privacy frameworks.
Excellent communication and collaboration skills.
Ability to work LATAM-based hours aligned with PST.
Nice to Have:
Familiarity with LLM frameworks (e.g., LangChain) or vector databases for AI/ML workloads.
Experience in healthcare or regulated environments (HIPAA familiarity is a plus).
Interest in mentorship and architectural decision-making at scale.
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