Enterprise AI System Developer

11 hours ago

Erode, Tamil Nadu, India beBeeArtificial Full time ₹ 30,00,000 - ₹ 40,00,000
AI Engineering Leadership

We are seeking a Senior AI Engineer or Research Scientist to lead the development and deployment of AI/ML systems at scale.

  • Key Responsibilities:
  • Design, implement, and deploy AI agents that operate reliably under enterprise constraints, such as multi-cloud environments, zero data egress, and compliance requirements.
  • Develop and test reasoning workflows, prompting strategies, and tool-use policies specifically 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, thereby avoiding reliance on toy datasets.
  • Create scalable pipelines for producing synthetic incidents—logs, metrics, traces—used for training and benchmarking in scenarios with limited data.
  • Collaborate closely with infrastructure engineers, SREs, and product leaders to incorporate AI research into the platform.

About the Role

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.

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).

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).

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