Enter a job title or keyword
Principal AI Engineer
Job Location:
Monthly Salary:
Posted:
21 August 2026 (2 days ago)
Posted:
Application Deadline:
18 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Principal AI Engineer
BuzzBoard Remote (WFH) Engineering / AIThe role
BuzzBoard builds AI products for the B2SMB market helping agencies media companies and sellers understand small businesses and market to them at a level of personalization that wasnt previously economical. Our platform is built on frontier models end to end. Not AI features bolted onto a legacy product the products are agent systems.
Our products span multi-agent marketing content generation real-time AI voice intake and pre- and post-sales intelligence for SMBs Zylo IRIS Ignite and Ember. They share a common internal pipeline for orchestration retrieval tool use evaluation and deployment.
This role owns the architecture underneath that pipeline. Not one feature the shared layer every product depends on: how retrieval is built and measured how agents reason and hold state and fail safely which models run where and what happens when one degrades how output quality is evaluated before it ships and where the line sits between what a model decides and what deterministic code decides. Youll set that architecture build the reusable patterns products inherit guide the engineers implementing them and own whether it holds up in production at volume.
Its a senior individual-contributor role. Two boundaries stated plainly:
What youll own
AI system architecture. Design the systems behind content generation business intelligence recommendations and agentic workflows including where the boundary sits between what a model decides and what deterministic code decides. Create reusable patterns prompts evaluation flows and orchestration layers that hold up across products rather than being rebuilt per feature.
Agentic workflows. Lead the design of agents that reason call tools and APIs manage state checkpoint and hand tasks off cleanly. Build the guardrails that keep them grounded and predictable the hard part is not making an agent act its making it fail safely and legibly when its wrong.
Retrieval and model strategy. Design RAG pipelines end to end: chunking embeddings metadata reranking and retrieval evaluation that actually measures whether the right context arrived. Own model selection across ecosystems on the axes that matter cost latency accuracy reliability and define the fallback and model-switching behavior for when a provider degrades.
Evaluation and quality. Stand up the evaluation frameworks that decide whether an AI output ships: output quality edit ratio hallucination rate schema adherence latency failure rate inference cost. Build regression testing so a prompt or model change cant silently break production. Turn the output feels off into a measured threshold.
Production readiness. Partner with engineering and platform teams so systems are deployable observable and maintainable. Package services (Python FastAPI/Flask Docker) when its the fastest path and diagnose the production failure modes specific to AI rate limits cost spikes model failures degraded output rather than escalating them blind.
Technical leadership. Mentor GenAI engineers review designs and prompts and workflows and evals and translate product requirements into architecture with measurable acceptance criteria. Communicate the tradeoffs to engineers and to leadership in language each can act on.
What were looking for
BuzzBoard Remote (WFH) Engineering / AIThe role
BuzzBoard builds AI products for the B2SMB market helping agencies media companies and sellers understand small businesses and market to them at a level of personalization that wasnt previously economical. Our platform is built on frontier models end to end. Not AI features bolted onto a legacy product the products are agent systems.
Our products span multi-agent marketing content generation real-time AI voice intake and pre- and post-sales intelligence for SMBs Zylo IRIS Ignite and Ember. They share a common internal pipeline for orchestration retrieval tool use evaluation and deployment.
This role owns the architecture underneath that pipeline. Not one feature the shared layer every product depends on: how retrieval is built and measured how agents reason and hold state and fail safely which models run where and what happens when one degrades how output quality is evaluated before it ships and where the line sits between what a model decides and what deterministic code decides. Youll set that architecture build the reusable patterns products inherit guide the engineers implementing them and own whether it holds up in production at volume.
Its a senior individual-contributor role. Two boundaries stated plainly:
- Not a research role. The deliverable is a shipped reliable capability not a paper or a demo.
- Not a DevOps role. You need to be fluent in deployment and able to get a service into production but full-time platform ownership sits elsewhere. Your center of gravity is AI system design.
What youll own
AI system architecture. Design the systems behind content generation business intelligence recommendations and agentic workflows including where the boundary sits between what a model decides and what deterministic code decides. Create reusable patterns prompts evaluation flows and orchestration layers that hold up across products rather than being rebuilt per feature.
Agentic workflows. Lead the design of agents that reason call tools and APIs manage state checkpoint and hand tasks off cleanly. Build the guardrails that keep them grounded and predictable the hard part is not making an agent act its making it fail safely and legibly when its wrong.
Retrieval and model strategy. Design RAG pipelines end to end: chunking embeddings metadata reranking and retrieval evaluation that actually measures whether the right context arrived. Own model selection across ecosystems on the axes that matter cost latency accuracy reliability and define the fallback and model-switching behavior for when a provider degrades.
Evaluation and quality. Stand up the evaluation frameworks that decide whether an AI output ships: output quality edit ratio hallucination rate schema adherence latency failure rate inference cost. Build regression testing so a prompt or model change cant silently break production. Turn the output feels off into a measured threshold.
Production readiness. Partner with engineering and platform teams so systems are deployable observable and maintainable. Package services (Python FastAPI/Flask Docker) when its the fastest path and diagnose the production failure modes specific to AI rate limits cost spikes model failures degraded output rather than escalating them blind.
Technical leadership. Mentor GenAI engineers review designs and prompts and workflows and evals and translate product requirements into architecture with measurable acceptance criteria. Communicate the tradeoffs to engineers and to leadership in language each can act on.
What were looking for
- 5 years in engineering AI ML or data-product work; 3 years hands-on with GenAI / LLM systems
- Production GenAI experience non-negotiable. You have built or scaled systems that carried real traffic and you can walk through one in detail: what broke what it cost what you changed.
- Deep working knowledge of LLMs and SLMs: prompt engineering structured outputs tool and function calling
- Hands-on with at least two major LLM ecosystems and able to reason about the tradeoffs between them rather than defaulting to the one you know
- RAG built for real: vector stores (Chroma Pinecone Weaviate FAISS or equivalent) embeddings semantic search and retrieval quality youve actually measured
- Hands-on with at least one agentic framework (LangGraph CrewAI AutoGen Semantic Kernel or equivalent) and the underlying concepts memory state tool integration failure handling well enough to move across frameworks
- Strong Python; comfortable with REST APIs Docker a cloud platform and basic CI/CD
- Demonstrated evaluation work: regression testing hallucination checks schema validation output scoring you can define metrics for quality reliability cost and business impact
- Track record guiding a small team or owning AI architecture end to end
- Comfort creating structure in a fast-moving environment with shifting requirements
- Fine-tuning or supervised training workflows
- SLM and open-source model deployment; model serving with vLLM Ollama or TensorRT-LLM
- Multimodal work across text image audio or video
- Kubernetes or serverless deployment
- Evaluation/observability tooling LangSmith MLflow Weights & Biases or equivalent
- Marketing technology SMB intelligence or content-automation domain experience
- Responsible AI privacy security and compliance practice
- Experience scaling AI systems that generate high volumes of content recommendations or insights
- Youve built or scaled real GenAI systems not just demos
- You design AI workflows that are reliable testable and cost-aware from the start
- You make practical model prompt retrieval and architecture calls and can defend them
- You know how to balance model intelligence against deterministic logic and engineering guardrails
- You stay hands-on while guiding engineers neither purely a manager nor purely an IC
- You think past which model to the whole system around the model
- Not a research or publications role
- Not full-time DevOps or platform ownership
- Not a role for someone whose GenAI track record is demos that never took production traffic
- Fully remote
- A production GenAI foundation already carrying real volume you scale it you dont start from zero
- Genuine architectural ownership over the agentic systems that come next
- A small high-context team that moves quickly and argues about the work
- Real impact on small businesses
Required Experience:
Staff IC
About Company
BuzzBoard fuels Demand Generation and Sales performance with SMB account intelligence and insights that can identify, segment, and score the accounts that matter.
