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Tier-1 brand and Bangalore location increase competition, niche AI/MLOps requirements moderate it.
ML architecture and MLOps skills are broadly transferable across industries, though power domain experience is a plus.
Multiple explicit must-haves (4+ years, 3+ ML years, cloud, Kubernetes, LLMs) increase strictness.
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Define and review end-to-end AI/ML solution architectures emphasizing modularity, performance, and maintainability.
Drive architectural alignment across components including model serving, orchestration, data pipelines, and cloud infrastructure.
Incorporate NFRs like scalability, latency, fault tolerance; evaluate cloud AI services (AWS, Azure); mentor teams on architectural best practices.
Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.
4+ years in software/system architecture with at least 3 years on ML-based solutions.
Experience designing scalable AI systems using Python, FastAPI, Docker, Kubernetes/ECS, and cloud-native AI tools (AWS/Azure).
Work Experience Required: 4+ years in software/system architecture including 3+ years with ML-based solutions.
Experienced in AI/ML system lifecycles, including model development, serving, monitoring, and retraining.
Familiar with architectures involving LLMs, Retrieval-Augmented Generation, and agent-based AI systems.
Prior exposure to Power/Energy or Electrification domain architecture is a strong plus; able to work across cross-functional teams.