GenAI Architect
4 days ago
Job Title: GenAI Architect
Experience Required: 10+ years in software architecture or machine learning roles, including at least 2+ years focused on Generative AI or Large Language Models (LLMs).
Role Overview
We are seeking a GenAI Architect to lead the design, development, and deployment of cutting-edge Generative AI solutions. This role requires deep expertise in LLMs, advanced AI architectures, and the ability to build scalable and production-ready systems leveraging the latest in AI technologies.
Key Responsibilities
- Architect and implement GenAI solutions, including LLMs, RAG pipelines, embedding models, and vector search.
- Design and optimize prompt engineering strategies, fine-tuning approaches, and mitigate hallucinations in AI outputs.
- Develop robust AI workflows using Python, LangChain, Hugging Face Transformers, and vector databases such as Pinecone or Weaviate.
- Integrate and leverage GenAI APIs (OpenAI, Azure OpenAI, Anthropic, Google Vertex AI).
- Deploy AI applications in cloud-native environments (AWS, GCP, Azure) using Kubernetes and containerized architectures.
- Lead model evaluation, performance tuning, and ensure scalable architecture for enterprise use cases.
- Collaborate with business stakeholders to translate complex AI concepts into actionable solutions and value-driven outcomes.
Required Skills & Qualifications
- 10+ years of experience in software architecture or machine learning, with 2+ years in GenAI/LLM-focused roles.
- Strong expertise in:
- LLMs and RAG pipelines
- Prompt engineering, fine-tuning, and hallucination mitigation
- LangChain, Hugging Face Transformers, Python
- Vector databases (Pinecone, Weaviate, etc.)
- Proficiency in deploying solutions on AWS/GCP/Azure with Kubernetes.
- Experience with model evaluation frameworks and industry best practices.
- Ability to communicate AI strategies and outcomes to non-technical stakeholders.
Preferred Qualifications
- Hands-on experience with LLMOps tools (Weights & Biases, MLflow, Trulens, PromptLayer).
- Knowledge of domain-specific AI applications (real estate, finance, risk modeling).
- Exposure to multi-modal GenAI (text, image, document, voice).
- Contributions to open-source projects, research publications, or patents in AI/ML.
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