Storage AI Architect

Key Responsibilities :

• Architect and develop enterprise-grade Generative AI, LLM and Agentic AI platforms/products, including LLM modules, inference pipelines, model-serving and AI orchestration frameworks.

• Own the LLM lifecycle - model selection, data preparation, fine-tuning, evaluation, deployment, monitoring and performance/cost optimization.

• Design and implement advanced RAG architectures, including data ingestion, embeddings, vector databases, hybrid search, reranking, context engineering, GraphRAG and Agentic RAG.

• Build Agentic AI and multi-agent systems with planning, reasoning, memory, tool/function calling, agent-to-agent communication, MCP integrations and human-in-the-loop workflows.

• Develop intelligent AI agents for storage management, monitoring, troubleshooting, capacity planning, anomaly detection, predictive analytics, RCA and infrastructure automation.

• Apply AI/ML, Deep Learning, NLP and Transformer-based architectures to complex storage, cloud and infrastructure problems.

• Design GenAIOps/MLOps capabilities for model deployment, evaluation, observability, governance and continuous improvement.

• Optimize AI systems for latency, throughput, scalability, GPU utilization, inference performance, token consumption and infrastructure cost.

• Design secure AI architectures addressing prompt injection, data leakage, agent authorization, tool security, AI supply-chain risks and Responsible AI.

• Architect scalable AI platforms across AWS/Azure/GCP and hybrid cloud, leveraging Kubernetes, containers, microservices and distributed systems.

• Integrate AI solutions with storage platforms, APIs, telemetry, logs, metrics, observability and enterprise data sources.

• Drive AI initiatives from research/PoC to enterprise-scale production, collaborating with Product, R&D, Engineering and Architecture teams.

Mandatory Skills :

• 10+ years of overall technology experience with strong experience in AI/ML, architecture and product engineering.

• 8+ years of hands-on AI/ML experience with significant GenAI/LLM development experience.

• Proven experience building LLM/GenAI platforms or products, beyond simply consuming AI APIs.

• Strong expertise in LLMs, Transformers, embeddings, RAG, Agentic AI and multi-agent systems.

• Hands-on experience with LLM modules, inference/model serving, fine-tuning, LoRA/PEFT, quantization and model optimization.

• Strong programming skills in Python.

• Hands-on experience with PyTorch/TensorFlow, Hugging Face, LangChain/LangGraph or equivalent frameworks.