AI Data Platform Engineer

Backend Engineer — AI & Data Infrastructure

We are looking for a Backend Engineer to build the AI and data infrastructure powering our next-generation talent and evaluation platform. You will work on data modeling, embeddings, feature extraction, pipeline creation, and serving AI-powered insights at scale.
 

This is a hands-on engineering role focused on scalable AI infrastructure — not research or prototyping. You will collaborate closely with engineering and product teams to design systems that process structured and unstructured data, generate semantic representations, and expose intelligence through reliable APIs.
 

Responsibilities
 

AI & Data Infrastructure
 

• Design and maintain data models for structured and unstructured data
• Build data ingestion and processing pipelines
• Implement embedding pipelines and vector search systems
• Develop feature extraction logic using LLMs and smaller models
• Optimize for accuracy, latency, and cost
   

Platform Engineering
 

• Design and implement backend services that expose AI-driven insights
• Maintain versioning, evaluation checks, and health monitoring for AI outputs
• Optimize performance, caching, and retrieval logic for semantic data
• Collaborate with engineers to ensure data consistency and reliability
   

Model Integration
 

• Integrate LLM APIs, embedding models, and small language models
• Build evaluation harnesses to validate extraction quality
• Monitor drift, degradation, and inconsistencies over time
   

General
 

• Write maintainable backend code (Node.js / Python / Go)
• Work cross-functionally with product and engineering teams
• Ensure AI systems are robust, observable, and production-ready
   

Required Skills
 

• Strong backend engineering experience
• Experience designing or maintaining data pipelines
• Practical experience with embeddings and vector databases
• Familiarity with LLM application patterns (prompting, extraction, RAG)
• Strong SQL and NoSQL fundamentals
• Ability to design scalable APIs
• Understanding of evaluation metrics for AI systems
   

Nice to Have
 

• Experience with LangChain or LlamaIndex
• Experience with smaller open-source models (Llama, Qwen, Mistral, etc.)
• Experience with ETL frameworks (Airflow, Dagster, Temporal)
• Interest in skills-based matching or intelligence systems (optional)
   

Success Indicators
 

• AI pipelines are stable, observable, and scalable
• Data processing is efficient and correct
• API services return reliable and consistent insights
• Embedding systems improve retrieval quality over time