Expert AI Systems Developer

13 hours ago

Pune, Maharashtra, India beBeeAI Full time ₹ 1,08,00,000 - ₹ 2,16,00,000
Agentic AI Engineer

We are seeking a highly skilled Agentic AI Engineer to join our team. The successful candidate will have a strong background in developing and deploying AI/ML systems at scale, with a focus on building intelligent agents that operate reliably under enterprise constraints.

Key Responsibilities:
  • Enterprise-Ready AI Development: Design, implement, and deploy AI agents that meet the demands of enterprise settings, including 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 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 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.

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