Offer summary

Qualifications:

Strong programming proficiency in Python and Typescript/React., Experience with cloud services like AWS or Azure and Infrastructure as Code (Terraform)., Knowledge of containerization technologies such as Docker and Kubernetes., Familiarity with MLOps tools and data engineering practices..

Key responsibilities:

Develop and maintain full-stack applications using Python and React.

Build and deploy backend web services and manage cloud infrastructure.

Implement CI/CD pipelines and ensure application logging and monitoring.

Collaborate with cross-functional teams to integrate services and optimize performance.

Job description

Company Description

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (19000+ experts across 33 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

Job Description

Must Have strong programming proficiency in Python (OOP, Async/Await, etc.).
Strong programming proficiency in Typescript/React and FE development experience. previous experience Build backend web services (FastAPI or similar).
Experience with cloud provider managed services (AWS, Azure) and IaC (Terraform).
Experience with containerization and container app deployments (Docker, Kubernetes, etc.).
Experience with CI/CD concepts and tooling (Github Actions, Gitlab CI, etc.).
Experience with application logging/monitoring (Sentry, ELK stack, Datadog, etc.).
Strong knowledge with service integrations and protocols (REST, streams, web sockets, webhooks).
Strong knowledge with distributed tasks using queues and consumers, pub/sub architecture.
Previous experience with vectors and embeddings (pgvector, chromadb).
Knowledge of LLM RAG/Agent core concepts and fundamentals.
Previous experience with MLOps/LLMOps tools (Arize Phoenix, LangSmith, MLFlow, W&B, etc.).
Data engineering experience (data modeling, data pipelines, event streaming, etc.).