AI Solution Architect

Job Description

About the Role:

We are seeking a highly experienced AI Solution Architect with deep expertise in Machine Learning (ML), Deep Learning (DL), Generative AI (Gen AI), and agentic AI approaches. The ideal candidate will design and implement scalable AI solutions that drive enterprise-wide analytics, intelligent automation, and advanced decision-making. This role requires strategic thinking to translate business needs into AI solutions using cloud-native tools, big data platforms, and advanced ML/DL frameworks.

Requirements

• Minimum 8 years to 15 years of experience in AI/ML/DL architecture, data science, and enterprise-level AI solutions.
• Strong experience in ML and DL, including CNNs, RNNs, Transformers, and GANs.
• Expertise in Generative AI, including LLMs and agentic AI approaches.
• Proficiency in Python, R, SQL, and ML/DL frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with MLOps, CI/CD pipelines, and Infrastructure-as-Code (IaC).
• Deep understanding of Data Lakes, Delta Lakes, and data pipelines for AI model training.
• Knowledge of AI governance, security, and compliance frameworks.
• Expertise in data processing using big data platforms and distributed computing frameworks.
• Excellent problem-solving skills, communication, and stakeholder management.
• Experience with deploying large-scale AI models using Kubernetes, Docker, and serverless architectures.
  
  

• Certifications: AI/ML certifications from recognized institutions or cloud providers.
• Experience with Generative AI: LLMs (e.g., GPT-4), generative art models, and autonomous agents.
• Knowledge of Agentic AI: Experience building intelligent agents using frameworks like LangChain or similar.
• Familiarity with Advanced AI Tools: OpenAI, Hugging Face, agentic AI libraries, and orchestration tools.
• Experience with Deep Learning Architectures: Proficiency in Transformers, GANs, and attention mechanisms.
• Multi-cloud & Hybrid Experience: Proficiency in designing solutions for multi-cloud or hybrid cloud environments.
• Understanding of Ethical AI: Knowledge of AI bias detection, model explainability and responsible AI practices.