What you’ll do:

● Build and maintain production services: design, implement, and operate reliable, well-tested backend services and APIs in Python and Go. Refactor or rewrite components when needed to improve performance, stability, and maintainability. ● Shape our architecture: define clear service boundaries and interfaces, guide communication patterns (REST/gRPC, events), and ensure strong observability, security, and resilience across our platform. ● Work with Golang in production: contribute to existing Go services and identify where migrating Python components to Go will reduce latency, increase throughput, or improve resource efficiency. ● Build ML/AI infrastructure: create data pipelines and services that support model training, evaluation, and deployment on large image datasets. ● Automate and ship: set up CI/CD, testing, and infrastructure-as-code to keep releases smooth and predictable across environments. ● Lead through impact: propose new directions, drive code and architecture improvements, mentor peers, and help raise the engineering bar across the team. ● Collaborate across disciplines: partner with scientists and engineers to understand problems end-to-end and deliver pragmatic, well-architected solutions.

Required (Must-have):

● M.Sc. in Computer Science/Engineering (or equivalent) or comparable industry experience.

● Expert production experience with Python and solid Golang experience building and running services.

● Deep understanding of microservices, distributed systems, and API design (REST/gRPC), with attention to clean contracts and failure modes.

● Hands-on experience with SQL and NoSQL databases, sound schema design and performance tuning, and familiarity with object storage (e.g., S3).

● Proven experience deploying and operating on Kubernetes and strong Unix/Linux fundamentals.

● Practical experience with AWS core services (EKS, EC2, S3, RDS, IAM, CloudWatch) and cost-aware architectures.

● Strong engineering practices including CI/CD, automated testing, code reviews, documentation, and a security-first mindset.

● Ability to lead technical initiatives, communicate trade-offs clearly, and collaborate effectively across multidisciplinary teams.

Desirable (Nice to have):

● Experience with Kubeflow, MLflow, Feast, Argo, Airflow, Ray, and model versioning/monitoring.

● Experience with digital pathology or large-scale image processing (e.g., whole-slide images) and tools such as OpenSlide, scikit-image, or OpenCV.

● Experience with performance engineering, including concurrency, memory profiling, high-throughput pipelines, and GPU/accelerator integration.

● Experience working in regulated environments (e.g., GxP), including data governance, privacy, and building software under regulated processes.

● Experience with JavaScript/TypeScript for internal tools and dashboards.

● Experience with event-driven systems and streaming platforms (e.g., Kafka).