Solutions Architect

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 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 : 10-12years 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.