Senior Python Engineer (AI Platform Services)(#5874)

N-iX is hiring for their Data Division's AI Platform Services team, which builds applications and platforms for customers using data in AI and ML projects.

• Experience making significant contributions to production backend systems, APIs, or services.
• Agentic development is a core part of how you work and can articulate the practices that make it effective.
• Strong experience with TypeScript and Python for server-side application development.
• Experience designing, building, and integrating GraphQL and REST APIs.
• Experience with relational databases such as PostgreSQL or MySQL, and NoSQL systems such as DynamoDB or Cloud Firestore.
• Experience building and operating serverless functions with AWS Lambda or Cloud Run functions.
• Experience with automated testing, debugging, and production support for distributed or service-based systems.
• Familiarity with Git-based collaboration and continuous-integration workflows.
• Strong communication skills and a constructive approach to pull-request reviews and peer mentorship.

Nice to have:

• Experience with GCP services such as Cloud Run and GKE for application workloads, Pub/Sub for asynchronous processing, and Cloud SQL.
• Experience with authentication, search, and cloud infrastructure.
• Experience with messaging, queues, and event-driven architectures.
• Experience building services for AI-enabled products and data-intensive workflows.
• Familiarity with observability tools and using production signals to guide performance and reliability improvements.
• Experience integrating services with external APIs and MCPs.

• Build and evolve reliable backend services, GraphQL and REST APIs, and integrations that power customer-facing and internal applications.
• Design and maintain clear API contracts and data-access patterns for application workflows.
• Work with relational and NoSQL databases to deliver dependable, scalable application behavior.
• Build asynchronous and event-driven processing where it improves product workflows and system resilience.
• Improve testing, observability, performance, and operational readiness for the systems you own.
• Own substantial work from implementation through deployment, production support, and incremental improvement.
• Partner with frontend engineers, product, design, and data teams to deliver useful product capabilities.
• Contribute to code reviews, shared engineering practices, and peer learning.