Chief AI Architect

2 days ago

Surat, Gujarat, India beBeeSenior Full time ₹ 1,50,00,000 - ₹ 3,00,00,000

About our team

We are building the world's first enterprise-ready incident copilot - intelligent, agentic systems that can detect, diagnose, and resolve complex production incidents across massive streams of logs, metrics, traces, and alerts.

Our vision is autonomous reliability for enterprise infrastructure. We combine AIOps with generative AI to achieve this goal.

Role Overview

We seek a seasoned developer with practical experience in developing and deploying AI/ML systems at scale. Your contributions will drive our platform's agentic intelligence, allowing it to conduct root cause analysis (RCA) and automated remediation in demanding enterprise settings.

This position bridges AI research and production engineering. You will experiment with large language models, reinforcement learning, and synthetic data, and implement these concepts into enterprise-grade, scalable, and dependable systems.

Key Responsibilities

  • Design, implement, and deploy AI agents that operate reliably under enterprise constraints.
  • Develop and test reasoning workflows, prompting strategies, and tool-use policies tailored for large-scale observability data.
  • Utilize techniques such as fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with real SRE workflows.
  • Create scalable pipelines for producing synthetic incidents used for training and benchmarking.
  • Closely collaborate with engineers, SREs, and product leaders to incorporate AI research into our platform.
  • Design systems with built-in observability, monitoring, and CI/CD processes ensuring failover, rollback, and resilience capabilities.
  • Stay up-to-date with the latest research in LLMs, agentic AI, and reliability, and incorporate these innovations into enterprise production environments.

Requirements

  • 5+ years of production experience building and deploying ML/AI systems at scale.
  • 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.

Nice to Have

  • Background in observability or large-scale production debugging.
  • Experience designing secure AI systems.
  • Contributions to open-source agent frameworks.
  • Experience with real-time ML serving.
  • Prior experience in enterprise SaaS or reliability-focused products.

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