Prompt Engineering Architect

Bright Vision Technologies•Edison, NJ

21h•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled Prompt Engineering Architect to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.