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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI Specialist - **Company:** Bright Vision Technologies - **Location:** Gilbert, AZ, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Applications Architecture, Application Frameworks, Computational Linguistics, Python (Programming Language), Software Engineering, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Generative AI, Indexer, Information Technology, Free and Open-Source Software - **Published:** July 12, 2026 - **Apply:** https://www.careerjet.com/jobad/us8d23b08bd2253358a7d1e8c26cda6a87 ## About the Role * Bachelor's or Master's degree in Computer Science, Computational Linguistics, or a related field. * Six or more years of software engineering experience, with significant time on LLM-based applications. * Demonstrated experience shipping LLM-powered products to production. * Deep familiarity with modern LLM APIs and agent frameworks. * Strong understanding of retrieval-augmented generation, embeddings, and vector databases. * Experience designing evaluation pipelines for non-deterministic systems. * Strong Python skills and comfort with modern application frameworks. * Solid grasp of responsible AI principles, including safety and policy considerations. * Excellent written and verbal communication skills. * Track record of mentoring engineers and influencing technical direction. Preferred Qualifications * Public writing, talks, or open-source contributions on LLM application development. * Experience with multi-agent architectures and complex tool-use systems. * Familiarity with fine-tuning workflows and when to choose them over prompting. * Exposure to product domains such as customer support, coding assistants, or analytics agents. * Experience integrating LLMs into enterprise software systems with strict compliance requirements. ## Description This role is part of Bright Vision Technologies' in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies - there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099. All our roles are W2. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience., * Define organization-wide standards, patterns, and reference architectures for LLM-based applications. * Design prompt structures, instruction templates, and retrieval strategies for diverse production use cases. * Architect agentic systems incorporating tool use, planning, memory, and multi-step reasoning. * Lead the design of retrieval-augmented generation pipelines including chunking, indexing, and reranking strategies. * Develop evaluation frameworks for prompt quality, agent reliability, and end-to-end task success. * Build internal tooling and libraries that accelerate LLM application development across teams. * Establish guardrails, safety filters, and policy enforcement patterns for LLM-powered products. * Collaborate with model engineering teams on prompt-model co-design and fine-tuning opportunities. * Conduct technical reviews of LLM application designs across multiple product teams. * Mentor engineers and applied scientists on prompt engineering and LLM application architecture. * Lead red-teaming exercises and continuously improve robustness against adversarial inputs. * Track latency, cost, and quality trade-offs in LLM application design and recommend optimizations. * Document patterns, anti-patterns, and lessons learned for broad internal reuse. * Stay current with LLM capabilities, tooling, and research, and translate advances into practical guidance. ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Optimizing Discovery: PostgreSQL's Role in Transforming GetYourGuide's Search](https://www.wearedevelopers.com/videos/1647-optimizing-discovery-postgresql-s-role-in-transforming-getyourguide-s-search) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac) - [Lessons Learned Building a GenAI Powered App](https://www.wearedevelopers.com/videos/1156-lessons-learned-building-a-genai-powered-app) - [Should we build Generative AI into our existing software?](https://www.wearedevelopers.com/videos/1129-should-we-build-generative-ai-into-our-existing-software) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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