Enterprise AI Development Lead

2 days ago

Belgaum, Karnataka, India beBeeArtificial Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Enterprise AI Development Lead

Job Overview:

We are seeking a seasoned Enterprise AI Development Lead to drive the development and deployment of scalable and reliable AI/ML systems. Your expertise will enable our platform to conduct root cause analysis (RCA) and automated remediation in demanding enterprise settings.

This position bridges AI research and production engineering, requiring you to experiment with LLMs, reinforcement learning, and synthetic data, and implement these concepts into enterprise-grade systems.

  • Design and Implement AI Systems: Design, develop, 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, 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 platform.
  • Production Excellence: Design systems with built-in observability, monitoring, and CI/CD processes, ensuring failover, rollback, and resilience capabilities.
  • Frontier Tracking: Stay up-to-date with the latest research in LLMs, agentic AI, and reliability, incorporating 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).
  • 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.

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