Team LeadSenior Specialist AI

BuzzBoard is looking for a Team Lead/Senior Specialist - AI to design guide and scale production-grade GenAI and agentic AI systems across our product ecosystem.

This is a senior technical leadership role focused on building intelligent AI systems that combine LLMs SLMs agents workflows retrieval evaluation and product intelligence. The ideal candidate has hands-on experience with production GenAI systems and can guide a small team in turning business problems into reliable scalable and cost-efficient AI capabilities.

This is not a pure research role and not a traditional DevOps role. We are looking for someone who can architect GenAI systems make smart model and framework choices guide implementation and ensure the systems work reliably in real product environments.

BuzzBoard already has production GenAI systems generating content and insights across multiple business workflows. This role will help scale that foundation into the next generation of agentic AI products.

• Architect scalable GenAI systems across content generation business intelligence recommendations automation and agentic workflows.
• Design multi-LLM and multi-agent systems using frameworks such as LangGraph CrewAI AutoGen or Semantic Kernel.
• Define architecture patterns for RAG tool calling function calling agent memory context management and workflow orchestration.
• Evaluate when to use LLM APIs SLMs fine-tuned models retrieval-based systems deterministic logic or hybrid approaches.
• Create reusable AI system patterns prompts evaluation flows and orchestration layers that can be used across products.

• Lead the design and development of agentic AI systems that can reason use tools call APIs manage state and complete multi-step tasks.
• Build and improve multi-agent collaboration patterns for use cases such as digital marketing SMB intelligence content generation brand analysis and campaign automation.
• Design guardrails to reduce hallucination improve factual grounding and ensure predictable outputs.
• Work on agent memory state persistence workflow checkpoints and task handoffs across AI components.

• Design and improve RAG pipelines using vector databases embeddings chunking strategies metadata reranking and retrieval evaluation.
• Guide fine-tuning or supervised training workflows where needed for specific business use cases.
• Optimize model selection across OpenAI Gemini Anthropic Hugging Face and open-source models based on cost latency accuracy and reliability.
• Define model-switching and fallback strategies for production systems.
• Improve prompt engineering structured outputs schema adherence and response consistency.

• Define AI quality evaluation frameworks for generated content summaries recommendations and agent outputs.
• Build or guide regression testing for prompts model changes workflow changes and release readiness.
• Track performance indicators such as output quality edit ratio hallucination rate latency failure rate and inference cost.
• Contribute to GenAI governance practices including responsible AI privacy safety and compliance.
• Work with Product and QA teams to define measurable acceptance criteria for AI outputs.

• Work with engineering and platform teams to ensure AI systems are deployable observable and maintainable in production.
• Provide hands-on support for packaging AI services using Python FastAPI/Flask Docker and cloud environments when needed.
• Understand basic CI/CD versioning logging monitoring rollbacks and environment management for AI services.
• Partner with DevOps or backend teams on deployment architecture scaling and reliability.
• Monitor and troubleshoot common production issues related to latency model failures API limits rate limits cost spikes and degraded output quality.

This role should be comfortable with deployment conversations but the primary expectation is AI system architecture and technical leadership not full-time DevOps ownership. The lighter deployment expectation keeps it distinct from the earlier AI Platform Specialist role which was more focused on Python deployment Docker cloud CI/CD monitoring scaling and rollbacks.

• Mentor GenAI engineers and guide technical decision-making across AI initiatives.
• Collaborate with Product Data Engineering Software Engineering QA and AI Operations teams.
• Translate product requirements into AI architecture implementation plans and measurable outcomes.
• Review AI designs prompts workflows evaluation outputs and architecture decisions.
• Communicate clearly with both technical and non-technical stakeholders.

• Strong hands-on experience building production GenAI or LLM-powered systems.
• Deep understanding of LLMs SLMs prompt engineering structured outputs tool calling and function calling.
• Experience with at least two major LLM ecosystems such as OpenAI Gemini Anthropic or Hugging Face.
• Experience building RAG systems using vector databases such as Chroma Pinecone Weaviate FAISS or equivalent.
• Strong understanding of embeddings semantic search retrieval quality and context design.

• Hands-on experience with agentic AI frameworks such as LangGraph CrewAI AutoGen or Microsoft Semantic Kernel.
• Experience building multi-step reasoning workflows agent pipelines or AI workflow automation.
• Understanding of agent memory tool integration state management and failure handling.
• Ability to design agent workflows that are reliable measurable and aligned with product outcomes.

• Strong Python skills with experience in AI/ML or backend development.
• Familiarity with FastAPI Flask Streamlit or similar frameworks.
• Working knowledge of REST APIs Docker cloud platforms and basic CI/CD workflows.
• Ability to work with engineering teams on deployment monitoring versioning and production debugging.
• Understanding of latency cost scalability observability and failure modes in AI systems.

• Experience designing evaluation methods for LLM outputs.
• Understanding of prompt regression testing hallucination checks schema validation and output scoring.
• Ability to define metrics for quality reliability cost and business impact.
• Familiarity with tools such as LangSmith MLflow Weights & Biases or equivalent is a plus.

• Ability to guide a small team of AI engineers or developers.
• Strong product thinking and ability to connect AI architecture to business outcomes.
• Clear communication with Product Engineering QA and leadership teams.
• Comfort working in a fast-moving startup-like environment with evolving requirements.

• Experience with fine-tuning or supervised training workflows.
• Experience with SLMs and open-source model deployment.
• Experience with model serving tools such as vLLM Ollama or TensorRT-LLM.
• Experience with multimodal AI involving text image audio or video.
• Familiarity with Kubernetes or serverless deployments.
• Experience in marketing technology SMB intelligence content automation or digital marketing platforms.
• Knowledge of responsible AI privacy security and compliance considerations.
• Prior experience scaling AI systems that generate high volumes of content recommendations or business insights.

• You have built or scaled real GenAI systems not just demos.
• You understand how to design AI workflows that are reliable testable and cost-aware.
• You can make practical model prompt retrieval and architecture decisions.
• You know how to balance LLM intelligence with deterministic logic and engineering guardrails.
• You can guide engineers while staying hands-on when needed.
• You are comfortable with ambiguity and can create structure in a fast-moving environment.
• You think beyond which model to use and focus on the full AI system around the model.

• 6 years of overall engineering AI ML or data product experience.
• 3 years of hands-on experience with GenAI LLMs NLP ML systems or AI-powered products.
• Prior experience leading AI architecture or guiding a team is strongly preferred.
• Production GenAI experience is required.