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Tier-1 brand, a generic SDE title, and Bangalore location together raise applicant density significantly.
Role demands specialized AI-platform, MLOps, and SRE expertise, limiting cross-industry transferability.
Extensive mandatory platform, MLOps, and infrastructure skills imply rigorous screening and technical gating.
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Design and build enterprise AI platforms for deploying, operating, and scaling LLM-powered applications including autonomous AI Agents and secure AI gateways.
Develop and maintain MLOps infrastructure for model serving, CI/CD pipelines, GPU-enabled scalable inference, and lifecycle management of AI models.
Create AI observability tools and autonomous operational systems for incident investigation, remediation, and capacity optimization at enterprise scale.
Strong experience with Kubernetes, AWS, Terraform, Docker, Helm, ArgoCD, GitOps, and platform engineering.
Proficient in programming with Python and Golang, with experience in distributed systems and API development.
Practical experience with AI frameworks and tools such as OpenAI APIs, LangChain, RAG, and MLOps technologies including MLflow, Kubeflow, and Triton Inference Server.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in AI-native application development and production-grade MLOps infrastructure involving LLMs and autonomous AI systems.
Able to operate at the intersection of AI engineering, platform engineering, site reliability engineering (SRE), and cloud infrastructure at an enterprise scale.
Technical proficiency in building scalable AI platforms with strong emphasis on automated workflows, model evaluation, and observability systems in a cloud-native environment.