Software Engineer

4 weeks ago

Itanagar, India Predigle Full time

About The Role
We are looking for a highly skilled Software Engineer to join our team and focus on the evaluation of Generative AI systems.

In this role, you will design and build robust back-end services, implement evaluation pipelines for GenAI models, and work closely with data scientists to ensure our AI systems are reliable, scalable, and production-ready.

This is an exciting opportunity for an engineer who thrives at the intersection of AI, back-end engineering, and system evaluation.

Key Responsibilities

  • Design, develop, and maintain scalable back-end services to support GenAI model evaluation and deployment.
  • Collaborate with data scientists and ML engineers to design evaluation frameworks, pipelines, and metrics for GenAI systems.
  • Implement APIs and services for automated model testing, benchmarking, and result reporting.
  • Apply clean coding practices, design patterns, and distributed systems knowledge to build robust evaluation workflows.
  • Integrate evaluation tools with FastAPI, LangChain, or agentic frameworks (where applicable).
  • Troubleshoot, optimize, and scale back-end systems to handle large-scale evaluations.
  • Document processes and ensure code quality through testing and peer reviews.

Requirements

  • 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.
  • Strong problem-solving skills with a structured and analytical mindset.

Nice To Have

  • Experience with FastAPI for lightweight API development.
  • Exposure to LangChain, agentic workflows, or GenAI frameworks.
  • Familiarity with LLM evaluation methodologies (A/B testing, LLM-as-a-judge, etc.).
  • Knowledge of cloud environments (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).

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