Remote Java Architect Jobs in Texas (NOW HIRING)

Job Title: Solutions Architect 3<\/b>
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Location: 100% Remote. <\/b>If they are residing in the US however, preferred location <\/b>is in Chicago, IL or Peoria, IL <\/b>area.
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Duration: 12+ months contract<\/b>
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This is Architecture position for AI related projects which require daily communications with business owners and other members of architecture team located in US.
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Position's Contributions to Work Group:<\/b>
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         <\/span><\/span><\/span><\/span>As a Principal Digital Architect, you will own the end\-to\-end architecture solutions for complex systems-balancing scalability, performance, security, and rapid delivery-while influencing Enterprise technology strategy.
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         <\/span><\/span><\/span><\/span>This role requires strong technical depth, architectural judgment, and the ability to translate ambiguous business needs into durable, scalable solutions.
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Typical task breakdown:<\/b>
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         <\/span><\/span><\/span><\/span>Own and define solution and platform architectures for large scale, distributed systems from concept through production.
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         <\/span><\/span><\/span><\/span>Create architecture that meets high standards for scalability, performance, resilience, and security.
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         <\/span><\/span><\/span><\/span>Partner closely with business leaders, product owners, engineering managers, and delivery teams to ensure architectural alignment with business outcomes.
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         <\/span><\/span><\/span><\/span>Assess, select, and introduce new technologies, including proof of concept development and architectural spikes.
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         <\/span><\/span><\/span><\/span>Establish and enforce architectural standards, patterns, and best practices across platform teams.
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         <\/span><\/span><\/span><\/span>Provide architectural guidance and mentorship to engineering teams, ensuring high quality implementation.
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         <\/span><\/span><\/span><\/span>Ensure solutions meet security, compliance, and regulatory requirements.
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         <\/span><\/span><\/span><\/span>Produce and maintain clear architecture documentation, including rationale and tradeoffs.
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         <\/span><\/span><\/span><\/span>Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency.
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Education & Experience Required:<\/b>
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         <\/span><\/span><\/span><\/span>Bachelor's degree with 5+ years' experience in this capacity.
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Required Technical Skills (Required):<\/b>
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         <\/span><\/span><\/span><\/span>Architectural Thinking<\/b>: Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade offs.
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         <\/span><\/span><\/span><\/span>Technical Leadership<\/b>: Influence without authority; guide teams through architectural decisions and implementation challenges.
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         <\/span><\/span><\/span><\/span>Communication<\/b>: Clearly articulate complex technical concepts to both technical and non\-technical stakeholders.
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         <\/span><\/span><\/span><\/span>Requirements Analysis<\/b>: Translate business and non\-functional requirements into scalable technical designs.
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         <\/span><\/span><\/span><\/span>Platform & Application Architecture<\/b>: Strong foundation in designing modern application and platform architectures using established patterns and standards.
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Consideration for top candidates:<\/b>
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         <\/span><\/span><\/span><\/span>Experience defining AI reference architectures and standards for enterprise adoption.
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         <\/span><\/span><\/span><\/span>Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non\-AI solutions.
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         <\/span><\/span><\/span><\/span>Proven experience taking AI systems from proof of concept to scaled production use.
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         <\/span><\/span><\/span><\/span>Strong programming background in Python and Java, with the ability to reason at code level.
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         <\/span><\/span><\/span><\/span>Proven experience designing and building enterprise scale, distributed systems<\/a>.
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         <\/span><\/span><\/span><\/span>Hands on experience with cloud native architectures<\/a>, including AWS services, containerization, and orchestration (Docker, Kubernetes).
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         <\/span><\/span><\/span><\/span>Deep understanding of data architecture<\/a>: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding.
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         <\/span><\/span><\/span><\/span>Experience with modern DevOps practices: CI\/CD, infrastructure as code, observability, and automated testing.
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         <\/span><\/span><\/span><\/span>Strong API design experience (REST, GraphQL, gRPC), including versioning and documentation.
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         <\/span><\/span><\/span><\/span>Ability to evaluate and introduce emerging technologies aligned to business goals.
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AI Related Skills:<\/b>
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Hands on experience designing Retrieval Augmented Generation (RAG) architectures, including:<\/b>
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         <\/span><\/span><\/span><\/span>Data ingestion pipelines.
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         <\/span><\/span><\/span><\/span>Document preprocessing and chunking strategies.
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         <\/span><\/span><\/span><\/span>Vectorization and embedding models.
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         <\/span><\/span><\/span><\/span>Query time retrieval, ranking, and context assembly.
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Deep understanding of embedding techniques, similarity search, and trade offs across:<\/b>
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         <\/span><\/span><\/span><\/span>Vector dimensions.
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         <\/span><\/span><\/span><\/span>Chunk size and overlap.
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         <\/span><\/span><\/span><\/span>Latency vs. recall vs. cost.
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         <\/span><\/span><\/span><\/span>Experience with vector databases and search layers (e.g., managed or self\-hosted vector stores) and their integration into application architectures.
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         <\/span><\/span><\/span><\/span>Experience with Agentic Frameworks.
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Ability to architect end to end AI workflows, including:<\/b>
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         <\/span><\/span><\/span><\/span>Prompt design and prompt versioning.
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         <\/span><\/span><\/span><\/span>Context management and memory patterns.
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         <\/span><\/span><\/span><\/span>Model routing and fallback strategies.
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Knowledge of LLM lifecycle considerations, including:<\/b>
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         <\/span><\/span><\/span><\/span>Model selection (hosted vs. self\-hosted).
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         <\/span><\/span><\/span><\/span>Fine tuning vs. RAG vs. hybrid approaches.
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         <\/span><\/span><\/span><\/span>Evaluation, monitoring, and drift detection.
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Strong understanding of AI system non\-functional requirements, including:<\/b>
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         <\/span><\/span><\/span><\/span>Performance and latency optimization.
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         <\/span><\/span><\/span><\/span>Cost controls and token efficiency.
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         <\/span><\/span><\/span><\/span>Security, data privacy, and guardrails
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         <\/span><\/span><\/span><\/span>Experience integrating AI capabilities into existing enterprise platforms via APIs and event driven architectures.
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         <\/span><\/span><\/span><\/span>Ability to assess, prototype, and productionize emerging AI technologies aligned to business use cases.
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Disqualifiers\/Red Flags\/Overqualifications:<\/b>
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         <\/span><\/span><\/span><\/span>Consistent job hopping\/ choppy tenure.
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         <\/span><\/span><\/span><\/span>If candidate is not local but open to relocation on their dime and being there in office day 1, please make that clear on the resume.
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