Job Description — Agentic AI Solution Architect
Position : Agentic AI Solution Architect
Location : Noida and Chennai only
Employment Type : Full-time
Experience level : (TEX) 12 yrs to 19 yrs
Relevant Exp : - 5+ yrs
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 BlueprintsAWS Bedrock / SageMaker with agent orchestrationAzure OpenAI Service with cognitive skills & pluginsGoogle Vertex AI AgentsOpen source frameworks : LangChain, Semantic Kernel, Haystack, Ray Serve for distributed orchestrationImplement 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 : 10-12years 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 .Contribution to open-source agentic frameworks or industrial AI toolkits.