Solutions Architect

Position

Location

Employment Type

Experience level : (TEX) 12 yrs to 19 yrs

Relevant Exp :- 5+ yrs

Position Overview

Generative AI Solution Architect

state-of-the-art generative and agentic AI

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.