Conversational AI Engineer

Bright Vision Technologies•Cary, NC

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking a Conversational AI Engineer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the organization. This role requires a deep practical mastery of modern LLMs combined with the discipline of building scalable, reusable design patterns, evaluation frameworks, and developer tooling. The ideal candidate will have a proven track record of shipping LLM-powered products in production, fluency in prompt design patterns and agent architectures, and strong judgment in applying LLM techniques responsibly.

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.