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.

- Experience with vector databases, semantic/hybrid search, knowledge graphs and GraphRAG.

- Strong understanding of GenAIOps/MLOps, AI evaluation, observability and governance.

- Strong experience with cloud, Kubernetes, distributed systems, microservices and scalable product architecture.

- Experience in storage, cloud infrastructure, distributed storage, telemetry or observability is highly preferred.