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.

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