Leadership - Agentic AI Solution Architect

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