Leadership - Agentic AI Solution Architect

Job Description

Position Overview We are seeking a highly skilled Generative AI Solution Architect with hands-on experience in Agentic AI frameworks and Small Language Models (SLMs) , along with deep expertise in AI/ML and data engineering in industrial contexts . This role will design, architect, and deploy scalable AI-driven solutions that leverage generative models, SLMs, autonomous agents, and robust data pipelines to transform operations in manufacturing, oil & gas, transportation, energy, smart infrastructure, and related verticals. You will be responsible for bridging state-of-the-art generative and agentic AI with operational efficiency at scale ?deploying solutions in cloud, hybrid, and industrial edge environments. Key Responsibilities 1. Generative AI, SLM & Agentic AI Solution Architecture Lead 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 (predictive analytics, operational assistance, workflow automation) that require low latency, reduced cost, privacy, and edge deployment . Architect multi-agent systems where SLMs serve as specialized reasoning components within larger orchestration frameworks. Ensure architectures meet industrial constraints ?including compute resource limits, security, redundancy, and real-time responsiveness. 2. Industrial AI/ML & Data Engineering Integration Design and implement scalable AI/ML workflows incorporating structured/unstructured data from sensors, IoT devices, time-series databases, and vision systems. Build data ingestion, cleaning, transformation, and feature store pipelines optimized for both generative AI and SLM training/inference. Leverage hybrid architectures that integrate edge inference (for SLMs) with centralized LLM-based services for decision support and large-context reasoning. Enable continuous learning pipelines with domain-specific fine-tuning and retraining of both LLMs and SLMs. 3. Agentic AI & Multi-Agent Systems Development Architect and deploy multi-agent solutions using: NVIDIA NIM?, NeMo?, and Agentic AI Blueprints AWS Bedrock / SageMaker with agent orchestration Azure OpenAI Service with cognitive skills & plugins Google Vertex AI Agents Open source frameworks: LangChain, Semantic Kernel, Haystack, Ray Serve for distributed orchestration