Agentic AI Solution Architect

As a highly skilled Generative AI Solution Architect, your role involves designing, architecting, and deploying scalable AI-driven solutions that leverage generative models, Small Language Models (SLMs), and autonomous agents to revolutionize operations in various industrial sectors. Key Responsibilities: - Lead the end-to-end design and deployment of generative AI systems using SLMs integrated with Agentic AI workflows in complex industrial settings. - Select and fine-tune Small Language Models for specific tasks such as predictive analytics, operational assistance, and workflow automation, ensuring low latency, reduced cost, privacy, and edge deployment. - Architect multi-agent systems where SLMs function as specialized reasoning components within larger orchestration frameworks, meeting industrial constraints like compute resource limits, security, redundancy, and real-time responsiveness. - Design and implement scalable AI/ML workflows by incorporating structured/unstructured data from various sources like sensors, IoT devices, time-series databases, and vision systems. - Build data pipelines for ingestion, cleaning, transformation, and feature store optimization, catering to generative AI and SLM training/inference needs. - Implement hybrid architectures that integrate edge inference for SLMs with centralized LLM-based services for decision support and large-context reasoning. - Facilitate continuous learning pipelines with domain-specific fine-tuning and retraining of both LLMs and SLMs. - Architect and deploy multi-agent solutions utilizing various tools and frameworks such as NVIDIA NIM, NeMo, Agentic AI Blueprints, AWS Bedrock/SageMaker, Azure OpenAI Service, Google Vertex AI Agents, and open-source frameworks like LangChain, Semantic Kernel, Haystack, and Ray Serve for distributed orchestration.,