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. Key Responsibilities
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, SageMaker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI. Data & Integration:
Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL 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. Preferred Attributes
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.
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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. Key Responsibilities
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, SageMaker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI. Data & Integration:
Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL 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. Preferred Attributes
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.
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