Backend Developer — Data-Oriented

Our client is a global leader in the development integration and implementation of advanced physical and cybersecurity intelligence and IT solutions delivering complete end-to-end solutions on the enterprise and national levels. They are now looking for an experienced Backend Developer ( MongoDB AI Agents) Data-Oriented.

Location: Europe
Type: Remote Full-time
Start date: ASAP

What youll do (AI-first):

• Own and implement the platforms core AI capabilities including:
  • Lesson generation (structured lesson plans sections exercises metadata)
  • Presentation generation (slide decks visuals prompts outline deck pipelines)
  • Content generation (explanations examples step-by-step guidance assessments)
  • Auto-grading (grading logic partial credit rules rubrics feedback generation)

• Build and maintain agent-based workflows to orchestrate multi-step AI tasks (tool calling task pipelines retries evaluation).
• Design AI systems that are reliable in production: rate limits fallbacks model routing prompt versioning structured outputs and validation/repair.
• Implement data pipelines that store AI outputs revisions and evaluation signals for continuous improvement.
• Build backend services in (JavaScript) and design scalable APIs to power AI features end-to-end.

• Work with unorganized / messy datasets and improve them over time (cleanup normalization migrations).
• Build analytics/BI-ready outputs from product and learning data (KPIs segmentation reporting endpoints).
• Optimize MongoDB performance (aggregations indexes) and implement caching (Redis or similar) for hot paths.

Requirements:

• Strong backend experience with and modern JavaScript.
• Proven experience building AI-native product features in production (not just demos).
• Hands-on experience with agent frameworks / agentic patterns (multi-step orchestration tool execution workflow graphs evaluation loops).
• Strong ability to implement structured AI pipelines: schema-driven generation output validation error recovery/repair versioning and observability.
• Experience with MongoDB (data modeling aggregations indexing performance tuning).
• Experience handling messy/unstructured data and evolving schemas safely.
• Experience with caching systems (Redis or similar) including invalidation strategies and performance thinking.
• Strong reliability mindset: retries/timeouts idempotency background jobs/queues monitoring/logging.

Preferred:

• Experience with grading systems (rubrics partial credit test-case style evaluation calibration).
• Background workers/queues streaming responses event-driven architecture.
• CI/CD automated testing for core workflows.
• Security best practices (auth permissions secrets management).

What success looks like

AI generation grading flows are stable fast and consistent at scale.

Lessons/presentations/content pipelines produce high-quality structured output with strong guardrails.

The system gracefully handles model failures rate limits and edge cases.

Messy data becomes usable and product insights are accessible for decision-making.