AI Solution Architect

Techno-Comp, Inc.
Raleigh, NC, United States
9 days ago
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Role details

Contract type
Contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Computing Cloud Engineering Computer Programming Continuous Integration Data Architecture Distributed Systems Python (Programming Language) Machine Learning
+23 more
OpenAI Tensorflow Enterprise Search Pinecone Google Cloud Pytorch LangChain Retrieval-Augmented Generation Large Language Models Prompt Engineering Google Vertex AI Llamaindex Generative AI Agentic-AI Event Driven Architecture Kubernetes HuggingFace Weaviate Milvus FAISS Restful APIs Docker Microservices

Job description

Job Summary: We are looking for an experienced AI Solution Architect to design and implement scalable, secure, and enterprise-grade AI solutions. The ideal candidate will have strong experience in Generative AI, LLMs, cloud platforms, machine learning, data architecture, and AI application development, along with the ability to translate business requirements into technical solutions.

Requirements

· 8+ years of experience in software/solution architecture, with strong experience in AI/ML solutions.

· Hands-on experience with Generative AI and Large Language Models (LLMs).

· Strong knowledge of RAG, embeddings, vector databases, prompt engineering, and AI agents.

· Experience with one or more cloud platforms: Azure, AWS, or Google Cloud Platform.

· Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent platforms.

· Strong programming experience in Python and/or Java.

· Experience with AI/ML frameworks such as LangChain, LlamaIndex, Hugging Face, PyTorch, or TensorFlow.

· Experience with vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, or Milvus.

· Strong understanding of REST APIs, microservices, event-driven architecture, and distributed systems.

· Experience with Docker, Kubernetes, CI/CD, and cloud-native architectures.

· Knowledge of AI security, responsible AI, data privacy, and model governance.

· Excellent communication, architecture documentation, and stakeholder-management skills.

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