Agentic AI Solution Architect

Job highlights

• Bachelor s / Master s in Computer Science,Data Science,AI,Industrial Engineering,or a related field
• . AI / ML & cloud certifications (AWS,Azure,GCP,NVIDIA DLI) preferred
• Experience deploying SLM agents in manufacturing,energy,or logistics for domain-specific automation

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

• Implement SLM-powered agents for specific industrial functions control logic, diagnostics, industrial documentation Q&A, safety monitoring, and autonomous alert resolution.

4. Platform & Ecosystem Expertise

• Deploy solutions across cloud and edge:

  • NVIDIA: NeMo, NIM , CUDA, TensorRT, TAO Toolkit, Metropolis for vision analytics.

  • AWS: Bedrock, Sagemaker, IoT Greengrass, Panorama.

  • Azure: Azure OpenAI, Cognitive Services, IoT Edge, Azure Stack HCI.

  • GCP: Vertex AI, AI Edge, Model Garden.

• Optimize for edge environments in industrial sites using SLMs to balance responsiveness, efficiency, and compliance.

5. Leadership, Collaboration & Governance

• Collaborate with AI researchers, data engineers, industrial automation engineers, and domain SMEs to align AI solutions with operational goals.

• Promote responsible AI governance ensuring SLM/LLM model auditability, explainability, bias mitigation, and regulatory compliance.

• Translate complex AI architectures into actionable implementation roadmaps and ROI-based business cases.

Qualifications & Experience

• Experience:

  • 10-12 years in AI/ML solution architecture or data engineering, with proven deployments in industrial/OT environments.

  • Hands-on implementation of SLM-powered agentic AI solutions.

  • Track record of deploying generative AI models (LLMs & SLMs) in both cloud and edge scenarios.

• Technical Skills:

  • Expertise in Small Language Models: design, fine-tuning, optimization (quantization, pruning, distillation) for constrained environments.

  • Proficiency with Agentic AI frameworks (NVIDIA NIM , NeMo , LangChain, Semantic Kernel, Ray Serve).

  • Strong in ML/DL frameworks (PyTorch, TensorFlow) and data pipeline tools (Airflow, Kubeflow, MLflow).

  • Knowledge of industrial AI integrations: SCADA, MES, PLCs, IoT sensors.

  • Deep understanding of edge AI constraints, security, and compliance.

• Business & Leadership Skills:

  • Ability to align AI strategy with operational KPIs.

  • Strong communication skills for technical and executive audiences.

• Education:

  • Bachelor s/Master s in Computer Science, Data Science, AI, Industrial Engineering, or a related field.

  • AI/ML & cloud certifications (AWS, Azure, GCP, NVIDIA DLI) preferred.

Preferred Attributes

• Experience deploying SLM agents in manufacturing, energy, or logistics for domain-specific automation.

• Published work or speaking engagements on SLM adoption, agentic AI, or industrial generative AI.

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