Solutions Architect

Job Description

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 scaledeploying 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 constraintsincluding 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 functionscontrol 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 governanceensuring 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.