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 insightsMaintain versioning, evaluation checks, and health monitoring for AI outputsOptimize performance, caching, and retrieval logic for semantic dataCollaborate with engineers to ensure data consistency and reliabilityModel Integration
Integrate LLM APIs, embedding models, and small language modelsBuild evaluation harnesses to validate extraction qualityMonitor drift, degradation, and inconsistencies over timeGeneral
Write maintainable backend code (Node.js / Python / Go)Work cross-functionally with product and engineering teamsEnsure AI systems are robust, observable, and production-readyRequired Skills
Strong backend engineering experienceExperience designing or maintaining data pipelinesPractical experience with embeddings and vector databasesFamiliarity with LLM application patterns (prompting, extraction, RAG)Strong SQL and NoSQL fundamentalsAbility to design scalable APIsUnderstanding of evaluation metrics for AI systemsNice to Have
Experience with LangChain or LlamaIndexExperience 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 scalableData processing is efficient and correctAPI services return reliable and consistent insightsEmbedding systems improve retrieval quality over time