Senior ai engineer

Location: Hyderabad/Bangalore/Singapore About Bug Raid. AI Incidents are the silent killers of modern enterprises. Every minute of downtime means lost revenue, lost trust, and engineers under fire. Bug Raid. AI is 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: autonomous reliability for enterprise infrastructure . Think AIOps meets generative AI , but designed from day one for security, scale, and production readiness . Role Overview We are seeking a  Senior AI Engineer  or Research Scientist w ith practical experience in developing and deploying AI/ML systems at scale. Your contributions will drive Bug Raid. AI’s agentic intelligence, allowing our platform 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 LLMs, reinforcement learning, and synthetic data, and also implement these concepts into  enterprise-grade, scalable, and dependable systems . Responsibilities Enterprise-Ready AI Development: Design, implement, and deploy 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 such as fine-tuning, RLHF, and reward modeling to ensure AI behavior aligns with  real SRE workflows, thereby avoiding reliance on toy datasets. Synthetic Data Generation: Create scalable pipelines for producing synthetic incidents—logs, metrics, traces—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 Bug Raid. 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, and incorporate these innovations into enterprise production environments. Requirements 5+ years of production experience building and deploying ML/AI systems at scale. Ph D 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 (Sage Maker, MLflow, Ray, Kubeflow). Comfort working with large-scale data streams (Kinesis, Kafka, Flink) and vector databases (Open Search, 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/Fed RAMP). Contributions to open-source agent frameworks (Lang Chain, Llama Index, Haystack, Ray Serve). Experience with real-time ML serving (low-latency inference, batch + streaming pipelines). Prior experience in enterprise Saa S or reliability-focused products. Why Join Bug Raid. AI Work on high-impact AI challenges that sit at the intersection of AI, Dev Ops, 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, Dev Ops, and large-scale systems. Competitive compensation, equity (ESOPs) , and a chance to join at an early stage with an outsized impact. A culture that values research rigor, engineering excellence, and execution speed .