GenAI Architect

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Work Location:

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GenAI Architect

Generative AI

Azure AI Foundry

AWS Bedrock

Retrieval-Augmented Generation (RAG)

Prompt Engineering

Agentic AI

Key Responsibilities

• Architect and implement advanced Generative AI models for text and image generation, RAG pipelines, prompt engineering, and synthetic data generation.
• Design RAG architecture, Agentic AI frameworks, and inference endpoints ensuring scalability and observability.
• Develop, fine-tune, and optimize LLMs (OpenAI, Azure OpenAI, open-source models) for business applications.
• Apply advanced prompt engineering techniques to improve AI accuracy, interpretability, and efficiency.
• Integrate generative AI models into existing applications using Python, Docker, Kubernetes, and LangChain.
• Implement data security, responsible AI, observability, and traceability across the AI pipeline.
• Collaborate with data engineers and developers to align model outcomes with product and business objectives.
• Evaluate and compare multiple LLMs using structured benchmarking and performance metrics.
• Stay ahead of industry trends in GenAI, RAG, and Agentic AI, driving adoption of new best practices.
  

Minimum Qualifications / Skills

• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related fields.
• 710 years total experience with at least 2 years in Generative AI solutioning.
• Strong proficiency in Python with experience in TensorFlow, PyTorch, and LangChain.
• Hands-on experience with Azure AI Foundry, AWS Bedrock, Azure AI Search, Prompt Flow, and Inference Endpoints.
• Deep understanding of RAG pipelines, prompt engineering, and zero-shot learning.
• Strong background in data science principles, statistical analysis, and model performance tuning.
  

Preferred Skills

• Experience building and deploying Agentic AI frameworks and LLM orchestration pipelines.
• Familiarity with NLP or Computer Vision for Generative AI applications.
• Knowledge of Kubernetes, Docker, and cloud-native AI deployment practices.
• Awareness of ethical AI, data security, and governance frameworks.