GenAI Model Evaluator

2 weeks ago

Delhi, Delhi, India beBeeBackend Full time ₹ 18,00,000 - ₹ 20,00,000
Backend Engineer Position

We are looking for a skilled Backend Engineer to join our team and contribute to the development of robust GenAI model evaluation workflows.

In this role, you will be responsible for designing, developing, and maintaining scalable back-end services that support the evaluation and deployment of GenAI models. You will work closely with data scientists and ML engineers to design evaluation frameworks and metrics, implement APIs and services for automated model testing, benchmarking, and result reporting, and troubleshoot, optimize, and scale back-end systems to handle large-scale evaluations.

To succeed in this position, you should have strong programming skills in Python with practical experience in building scalable services, solid experience with Java (Spring Boot) for developing enterprise-grade back-end applications, proven expertise in back-end engineering: clean code, design patterns, and distributed systems, experience in building and deploying APIs, microservices, and evaluation pipelines, and strong problem-solving skills with a structured and analytical mindset.

Requirements:

  • Programming Skills: Strong Python skills with hands-on experience in building scalable services.
  • Java Experience: Solid experience with Java (Spring Boot) for developing enterprise-grade back-end applications.
  • Back-End Engineering: Proven expertise in back-end engineering: clean code, design patterns, and distributed systems.
  • Pipeline Development: Experience in building and deploying APIs, microservices, and evaluation pipelines.
  • Problem-Solving: Strong problem-solving skills with a structured and analytical mindset.

Bonus Skills:

  • FastAPI Experience: Familiarity with FastAPI for lightweight API development.
  • LLM Evaluation: Knowledge of LLM evaluation methodologies (A/B testing, LLM-as-a-judge, etc.).
  • Cloud Environments: Familiarity with cloud environments (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).

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