Team Lead/Senior Specialist - AI

BuzzBoard

10d

About The Position

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.

Requirements

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.
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.
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.
Production GenAI experience is required.

Nice To Haves

Familiarity with tools such as LangSmith, MLflow, Weights & Biases, or equivalent is a plus.
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.
Prior experience leading AI architecture or guiding a team is strongly preferred.

Responsibilities

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.
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.