AI Systems Architect

1 day ago

Erode, Tamil Nadu, India beBeeArtificialIntelligence Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Enterprise AI Engineering Role

We are seeking a seasoned AI Engineer with expertise in designing and implementing AI solutions that meet the demands of large-scale enterprise environments.

  • Develop robust reasoning workflows, prompting strategies, and tool-use policies tailored for massive observability data sets.
  • Utilize techniques such as fine-tuning, reinforcement learning from human feedback (RLHF), and reward modeling to ensure AI behavior aligns with real SRE workflows.
  • Create scalable pipelines for generating synthetic incidents—logs, metrics, traces—to train and benchmark AI systems in scenarios with limited data availability.
  • Collaborate closely with infrastructure engineers, SREs, and product leaders to integrate AI research into our platform.
  • Design systems with built-in observability, monitoring, and CI/CD processes to ensure failover, rollback, and resilience capabilities.
  • Stay up-to-date with the latest research in large language models, agentic AI, and reliability, and apply these innovations to production environments.
Requirements
  • 5+ years of experience developing and deploying ML/AI systems at scale.
  • PhD or Master's degree 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).
  • Contributions to open-source agent frameworks.
  • 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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