GEN 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.

• Design and architect scalable, secure, and high-performing AI/GenAI solutions leveraging cloud-native services and frameworks.
• Integrate AI systems with enterprise data platforms, applications, APIs, and workflow systems ensuring end-to-end traceability and performance.
• GenAI Solution Design:
• Architect and implement Generative AI solutions using LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, etc.) for use cases like knowledge retrieval, document summarization, copilots, and content generation.
• MLOps & LLMOps:
• Define and implement model lifecycle management pipelines (CI/CD for ML/LLMs), governance, observability, and responsible AI frameworks.
• Data Architecture
  
  Collaboration:
  
  
• Work closely with Data Engineering teams to design and optimize feature stores, vector databases, embeddings, and multimodal data pipelines.
• Stakeholder Engagement:
• Performance Optimization:
• GenAI & LLMs: Experience with GPT-4/4o, Claude, Gemini, Mistral, LLaMA, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
• 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, Weaviate, FAISS, Milvus, or Cosmos DB.
• Responsible AI: Understanding of bias detection, model explainability, and governance principles.
• Architecture Design: Expertise in TOGAF/Well-Architected frameworks and AI reference architectures.

• Demonstrated success in implementing GenAI copilots, chatbots, or RAG systems.
• Ability to balance innovation with pragmatic business value delivery.
• Passion for continuous learning and AI community engagement.