Artificial Intelligence Specialist

AI Architect

A highly experienced AI Architect with 10–12 years of professional experience in designing, building, and scaling enterprise-grade AI and Generative AI (GenAI) solutions across leading public cloud platforms (Azure, AWS, GCP). The ideal candidate will play a strategic role in AI solution architecture, data & model engineering, cloud integration, and MLOps/LLMOps frameworks, while working closely with business and technology teams to drive measurable outcomes through AI innovation.

• AI Solution Architecture:
• Enterprise Integration:
• GenAI Solution Design:
• MLOps & LLMOps:
• Data Architecture
  
  Collaboration:
• Stakeholder Engagement:
• Innovation & Research:
• Performance Optimization:
• Mentorship:

• AI/ML Frameworks:
  Tensor Flow, PyTorch, Scikit-learn, Hugging Face, Lang Chain, Llama Index, Open AI API, etc.
• GenAI & LLMs:
  Experience with GPT-4/4o, Claude, Gemini, Mistral, LLaMA, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
• Cloud Platforms:
  Deep understanding of Azure AI (OpenAI, AI Search, ML), AWS (Sage Maker, Bedrock, Comprehend, Lex), GCP Vertex AI.
• MLOps & LLMOps:
  Azure ML Pipelines, Sage Maker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
• Data & Integration:
  
  Experience with Data Lakes, Databricks, Synapse, Kafka, REST/Graph
  
  QL APIs, and vector DBs like Pinecone, We aviate, FAISS, Milvus, or Cosmos DB.
• Programming:
  Python, SQL, REST APIs, YAML, Docker, Kubernetes, Terraform (IaC).
• Responsible AI:
  Understanding of bias detection, model explain ability, and governance principles.
• Architecture Design:
  Expertise in TOGAF/Well-Architected frameworks and AI reference architectures.

• Proven experience in designing and delivering AI solutions at enterprise scale.
• Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
• Strong communication and presentation skills for stakeholder engagement and executive alignment.