Backend AI & Data Pipeline Engineer

About the role

We are looking for a Backend AI & Data Pipeline Engineer to own the end-to-end data processing infrastructure that powers Yuzee's intelligent course and job matching platform. You will design and maintain scalable, event-driven pipelines that process tens of thousands of daily records, generate semantic embeddings, and feed a growing knowledge graph used for personalised career pathway recommendations.

What you'll do

• Design and maintain three distinct processing pipelines — scheduled job ingestion, event-driven course processing, and a periodic knowledge graph builder — each with independent trigger logic and cost controls
• Generate and manage semantic embeddings via Amazon Bedrock (Titan v2), index them in MongoDB Atlas Vector Search, and calibrate similarity thresholds to ensure match accuracy
• Build and maintain a knowledge graph linking jobs, courses, skills, and industries using FP-Growth association rules and archetype-to-SOC code mapping
• Build and improve a two-stage discovery and matching API on AWS Lambda — vector retrieval first, then deep eligibility scoring with LLM re-ranking
• Right-size Fargate Spot instances and design resumable processing loops that tolerate interruption, keeping infrastructure costs under control as data volume scales
• Maintain and improve daily job scrapers across multiple sources and build institution data scrapers with robust HTML cleaning pipelines

What we're looking for

• 1+ years of backend engineering experience focused on data pipelines, ML infrastructure, or search systems
• Hands-on experience with AWS serverless and container services — Lambda, ECS Fargate, EventBridge, and Step Functions
• Strong Python skills — Pandas, async processing, bulk database operations, and text cleaning
• Familiarity with vector databases and semantic similarity search; MongoDB Atlas Vector Search experience is a strong plus
• Cost-conscious infrastructure mindset — you think in per-record compute costs, free tiers, Spot resilience, and right-sizing
• Ability to document and communicate complex architecture clearly to both technical and non-technical stakeholders

Nice to have

• Experience with knowledge graphs or association rule mining (FP-Growth, Apriori)
• Experience using LLMs for re-ranking or eligibility assessment on top of vector retrieval results
• Background in edtech, jobtech, or recommendation/matching systems

Degree or existing proven experience 

Use this instead:

Benefits

• Fully remote / work-from-home role

• Flexible working hours within the team’s expected schedule and business needs

• Opportunity to work on real backend, data, and AI infrastructure projects

• Exposure to practical engineering challenges in scraping, pipelines, retrieval, and cloud systems

• Ongoing growth and development within a fast-moving technology environment

• Opportunity to build long-term value and grow with the company based on performance, including progression and increased responsibility over time

A slightly more polished version:

Benefits

• Fully remote / work-from-home position

• Some flexibility in working hours, depending on team requirements and deliverables

• Hands-on experience working on meaningful backend, data pipeline, and AI-related systems

• Opportunity to contribute to a growing platform with real product and engineering challenges

• Professional growth in a practical, fast-paced environment

• Strong potential for long-term progression based on performance, regardless of location

If you want it to sound more attractive for hiring, the strongest version would be to add things like:

• ownership

• real product impact

• career growth

• direct exposure to architecture and scaling decisions