AI Solutions Architect (W2 Only | Onsite)
Raas Infotek LLC
Richmond, United States
2 months ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Amazon Web Services
Computer Vision
Microsoft Azure
Cloud Computing
Python (Programming Language)
Machine Learning
Natural Language Processing
Tensorflow
Software Deployment
Google Cloud
Pytorch
+10 more
Large Language Models
Snowflake
IT Architecture
Deep Learning
Generative AI
Information Technology
HuggingFace
Machine Learning Operations
Docker
Databricks
Job description
We are seeking an experienced AI Solutions Architect to lead the design and implementation of enterprise AI and Generative AI solutions. The ideal candidate will have deep expertise in Machine Learning, Large Language Models (LLMs), cloud platforms, and AI architecture while guiding development teams in delivering scalable, secure, and production-ready AI applications., * Design enterprise AI and Generative AI architectures.
- Build scalable AI solutions using LLMs and RAG frameworks.
- Define AI architecture standards and best practices.
- Lead technical discussions and mentor engineering teams.
- Collaborate with business leaders to identify AI opportunities.
- Oversee AI model deployment, monitoring, and optimization.
- Ensure security, compliance, and governance of AI solutions.
- Drive innovation and adoption of modern AI technologies.
Requirements
- 15+ years of overall IT experience.
- 5+ years of experience designing AI/ML solutions.
- Strong experience with Python.
- Hands-on experience with Generative AI and Large Language Models (LLMs).
- Experience with Retrieval-Augmented Generation (RAG).
- Experience with LangChain and/or LangGraph.
- Experience with OpenAI, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
- Strong knowledge of vector databases such as Pinecone, FAISS, ChromaDB, or Weaviate.
- Experience deploying AI applications using Docker and Kubernetes.
- Cloud experience with AWS, Azure, or Google Cloud Platform.
- Experience with MLOps tools and AI deployment pipelines.
- Strong understanding of API integration and enterprise architecture., * TensorFlow or PyTorch.
- Hugging Face Transformers.
- NLP, Deep Learning, and Computer Vision.
- Databricks or Snowflake.
- Experience with AI governance, security, and Responsible AI.
- Strong leadership and stakeholder management skills.
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