AI System Developer

1 week ago

Shimoga, Karnataka, India beBeeArtificialintelligence Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
Enterprise AI Engineer Role

About BugRaid.AI:

  • Building the world's first enterprise-ready incident copilot.
  • Intelligent, agentic systems that detect, diagnose, and resolve complex production incidents.

We envision autonomous reliability for enterprise infrastructure.

Imagine AIOps meets generative AI, designed from day one for security, scale, and production readiness.

This role bridges AI research and production engineering.

  • Experiment with LLMs, reinforcement learning, and synthetic data.
  • Implement these concepts into enterprise-grade, scalable, and dependable systems.
Responsibilities
  1. Designing, implementing, and deploying AI agents that operate reliably under enterprise constraints.
  2. Developing and testing reasoning workflows, prompting strategies, and tool-use policies tailored for large-scale observability data.
  3. Utilizing techniques like fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with real SRE workflows.
  4. Creating scalable pipelines for producing synthetic incidents—logs, metrics, traces—used for training and benchmarking in scenarios with limited data.
  5. Cross-team collaboration with infrastructure engineers, SREs, and product leaders to incorporate AI research into the platform.
  6. Designing systems with built-in observability, monitoring, and CI/CD processes, ensuring failover, rollback, and resilience capabilities.
  7. Staying up-to-date with the latest research in LLMs, agentic AI, and reliability, and incorporating these innovations into enterprise production environments.
Requirements
  • 5+ years of production experience building and deploying ML/AI systems at scale.
  • A PhD or Master's in Computer Science, Machine Learning, or related fields (or equivalent experience).
  • A strong foundation in deep learning, reinforcement learning, or robotics.
  • Hands-on experience with LLM fine-tuning, RLHF, or synthetic data generation.
  • A proven track record of shipping AI systems into production.
  • Experience with cloud-native architectures, Kubernetes, and MLOps tooling.
  • Comfort working with large-scale data streams and vector databases.
  • Strong collaboration skills and ability to communicate across research, engineering, and product teams.

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