Solutions Architect 3
J
JobgetherEnterprise AI
100% remote opportunity for candidates residing in the United States.ContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in an architecture role, including at least 5–6 years of hands-on AI architecture experience.
- Required Skills
- AWSDockerPythonJavaKubernetesGenerative AI
Requirements
- Bachelor’s degree.
- 5+ years of experience in an architecture role, including at least 5–6 years of hands-on AI architecture experience.
- Experience designing, owning, and scaling AI solutions at the architecture level, with strong Generative AI and RAG expertise.
- Experience taking AI solutions from proof of concept through production and scaled enterprise adoption.
- Hands-on experience with RAG architectures, vectorization, embedding models, similarity search, retrieval strategies, and context assembly.
- Experience with agentic AI frameworks and end-to-end AI workflow architecture.
- Strong foundation in modern application and platform architecture, including enterprise-scale distributed systems.
- Strong programming background in Python and Java, with the ability to reason at code level.
- Hands-on experience with cloud-native architectures, including AWS, Docker, and Kubernetes.
- Deep understanding of data architecture, including SQL and NoSQL databases, data warehouses such as Snowflake, data modeling, replication, and sharding.
- Experience with modern DevOps practices, including CI/CD, infrastructure as code, observability, and automated testing.
- Strong API design experience with REST, GraphQL, or gRPC, including versioning and documentation.
Responsibilities
- Own and define end-to-end solution and platform architectures for large-scale distributed systems from concept through production.
- Design architectures that meet scalability, performance, resilience, security, and cost-efficiency requirements.
- Translate business and non-functional requirements into durable technical solutions.
- Define AI reference architectures and enterprise standards, including Generative AI, RAG, and agentic frameworks.
- Evaluate, prototype, and introduce emerging technologies through proofs of concept and architectural spikes.
- Establish architecture standards, patterns, and best practices, and provide technical guidance to engineering teams.
- Architect end-to-end AI workflows covering prompt design, context management, model routing, retrieval, evaluation, monitoring, and lifecycle management.
- Design RAG pipelines covering data ingestion, document preprocessing, chunking, embeddings, retrieval, ranking, and context assembly.
- Integrate AI capabilities into enterprise platforms through APIs and event-driven architectures.
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