Strategic AI Innovator

About Incident Response

Incidents can be catastrophic for modern businesses, resulting in revenue loss, trust erosion, and pressure on engineering teams.

BugRaid.AI is developing an incident copilot – intelligent systems that detect, diagnose, and resolve complex production incidents across massive data streams.

Our vision: autonomous infrastructure reliability, combining AIOps and generative AI for security, scalability, and production-readiness.

Job Summary

We are seeking a Strategic AI Innovator with hands-on experience in designing and deploying AI/ML systems at scale. Your work will drive BugRaid.AI's agentic intelligence, enabling root cause analysis (RCA) and automated remediation in demanding enterprise settings.

This role bridges AI research and production engineering. You will experiment with large language models (LLMs), reinforcement learning, and synthetic data, and implement these concepts into scalable and dependable systems.

Main Responsibilities

• Designing Enterprise-Ready AI Solutions: Develop AI agents that operate reliably under enterprise constraints, such as multi-cloud environments, zero-data egress, and compliance requirements.
• LLM & Agent Research: Develop and test reasoning workflows, prompting strategies, and tool-use policies specifically tailored for large-scale observability data.
• Training & Alignment: Utilize techniques like fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with real SRE workflows, avoiding reliance on toy datasets.
• 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.AI platform.
• Production Excellence: Design systems with built-in observability, monitoring, and CI/CD processes, ensuring failover, rollback, and resilience capabilities.
• Frontier Tracking: Keep up with the latest research in LLMs, agentic AI, and reliability, incorporating innovations into 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 (not just research prototypes).
• 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 (logs, metrics, traces) or large-scale production debugging.
• Experience designing secure AI systems (data privacy, zero-data egress, compliance frameworks like HIPAA/FedRAMP).
• Contributions to open-source agent frameworks (LangChain, LlamaIndex, Haystack, Ray Serve).
• Experience with real-time ML serving (low-latency inference, batch + streaming pipelines).
• Prior experience in enterprise SaaS or reliability-focused products.

Why Work with Us

• Work on high-impact AI challenges that bridge AI, DevOps, and enterprise reliability.
• Shape the core intelligence layer of a product designed to be enterprise-ready from day one.
• Collaborate with a world-class founding team experienced in AI, DevOps, and large-scale systems.
• Competitive compensation and equity opportunities.
• A culture that values research rigor, engineering excellence, and execution speed.