Advanced Incident Resolution Specialist

2 weeks ago

Surat, Gujarat, India beBeeReliability Full time US$ 2,00,000 - US$ 2,50,000
Incident Resolution Specialist

Incidents in modern enterprises can have devastating consequences, resulting in lost revenue and eroded trust.

BugRaid.AI is a pioneer in developing intelligent systems that detect, diagnose, and resolve complex production incidents across vast data streams.

Our mission is to establish autonomous reliability for enterprise infrastructure by integrating cutting-edge technologies like AIOps and generative AI.

Key Responsibilities
  • AI Development: Design, implement, and deploy AI agents that operate reliably under enterprise constraints, including multi-cloud environments and compliance requirements.
  • LLM Research: Develop and test reasoning workflows, prompting strategies, and tool-use policies tailored for large-scale observability data.
  • Training & Alignment: Utilize techniques such as fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with real SRE workflows.
  • Synthetic Data Generation: Create scalable pipelines for producing synthetic incidents used for training and benchmarking in scenarios with limited data.
  • Cross-Team Collaboration: Collaborate closely with infrastructure engineers, SREs, and product leaders to incorporate AI research into the BugRaid platform.
  • Production Excellence: Design systems with built-in observability, monitoring, and CI/CD processes, ensuring failover, rollback, and resilience capabilities.
  • Frontier Tracking: Stay current with the latest research in LLMs, agentic AI, and reliability, and apply these innovations to enterprise production environments.
Requirements
  • 5+ years of experience building and deploying ML/AI systems at scale.
  • PhD or Master's in Computer Science, Machine Learning, or related fields (or equivalent experience).
  • Strong foundation in deep learning, reinforcement learning, or robotics.
  • Hands-on experience with LLM fine-tuning, RLHF, or synthetic data generation.
  • Proven track record of shipping AI systems into production.
  • Experience with cloud-native architectures (AWS, Azure, GCP), Kubernetes, and MLOps tooling (SageMaker, MLflow, Ray, Kubeflow).
  • Comfort working with large-scale data streams (Kinesis, Kafka, Flink) and vector databases (OpenSearch, Pinecone, Weaviate).
  • Strong collaboration skills and ability to communicate across research, engineering, and product teams.
Nice to Have
  • Background in observability or large-scale production debugging.
  • Experience designing secure AI systems (data privacy, zero-data egress, compliance frameworks).
  • Contributions to open-source agent frameworks.
  • Experience with real-time ML serving (low-latency inference, batch + streaming pipelines).
  • Prior experience in enterprise SaaS or reliability-focused products.

The ideal candidate will possess a strong technical background, excellent collaboration skills, and a passion for pushing the boundaries of AI innovation.

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