Agentic AI Solution Architect

Role Overview: You are a highly skilled Generative AI Solution Architect with expertise in Agentic AI frameworks and Small Language Models (SLMs). Your role involves designing, architecting, and deploying scalable AI-driven solutions in industrial contexts to enhance operations in various sectors such as manufacturing, oil & gas, transportation, energy, smart infrastructure, and related verticals. Key Responsibilities: - Lead the end-to-end design and deployment of generative AI systems using LLMs and SLMs, integrated with Agentic AI workflows for autonomous reasoning, planning, and execution in complex industrial environments. - Select and fine-tune Small Language Models for domain-specific tasks, ensuring low latency, reduced cost, privacy, and edge deployment requirements are met. - Architect multi-agent systems where SLMs function as specialized reasoning components within larger orchestration frameworks, considering industrial constraints like compute resource limits, security, redundancy, and real-time responsiveness. - Design and implement scalable AI/ML workflows incorporating structured/unstructured data from various sources like sensors, IoT devices, time-series databases, and vision systems. - Build data pipelines optimized for generative AI and SLM training/inference, enabling continuous learning pipelines with domain-specific fine-tuning and retraining of both LLMs and SLMs. - Leverage hybrid architectures that integrate edge inference for SLMs with centralized LLM-based services for decision support and large-context reasoning. - Architect and deploy multi-agent solutions using a variety of 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, Ray Serve for distributed orchestration.,