As a Staff AI Product Solutions Manager, you will identify, shape, and advance enterprise AI opportunities to deliver measurable business value. Working across technology, architecture, business, risk, and delivery teams, you will act as a strategic value orchestrator and context engineer. Your daily responsibilities include defining use cases, leading discovery and rapid experimentation using synthetic user personas, and conducting build-versus-buy assessments for open-weights models, commercial foundation models, and fine-tuned Small Language Models. You will author context specs and agent playbooks while managing probabilistic tolerances for multi-agent systems. Key technical competencies include knowledge of Large Language Models, retrieval-augmented generation, vector databases, and agent orchestration. You will translate validated solutions into reusable patterns, balancing model accuracy with latency and compute costs to solve complex enterprise problems.
Identify and prioritize AI opportunities based on business value, feasibility, risk, and organizational readiness.
Define specific AI use cases, target outcomes, success measures, and user adoption considerations for enterprise capabilities.
Lead discovery and rapid experimentation using synthetic personas and simulations to validate model performance and cost-to-serve.
Conduct build-versus-buy assessments for AI platforms, open-weights models, and commercial foundation models.
Establish product direction for agentic experiences, including guardrails, human-in-the-loop workflows, and performance thresholds.
Author context specs and agent playbooks while managing probabilistic tolerances for multi-agent systems.
Translate validated AI solutions into capability briefs and reusable patterns for delivery teams to scale.
Monitor unit economics of compute, token costs, and accuracy metrics to recommend scaling or retiring solutions.
What we're looking for
Bachelor’s degree in Business, Technology, Data/Analytics, Computer Science, or related field; equivalent experience considered.
7+ years of experience in product strategy, managing concurrent product portfolios, digital transformation, technology, or related work.
3+ years leading AI, machine learning, data platform, or other complex technology initiatives.
Experience taking AI or technology opportunities from discovery through implementation and measurement.
Knowledge of modern AI concepts, including Large Language Models (LLMs), retrieval-augmented generation (RAG), model evaluation, and non-deterministic systems.
Experience assessing value, feasibility, risk, and investment tradeoffs across cross-functional partners.
Experience using business, product, operational, or financial metrics to guide decisions.
Advanced degree, certification, or training in a relevant business or technical discipline (preferred).
Generative AI
Agentic AI
Retrieval-Augmented Generation (RAG)
Python
SQL
Docker
Git
LangChain
LangGraph
Hugging Face
Ollama
vLLM
OpenAI APIs
Llama
Mistral
DeepSeek
Gemma
Qwen
Model Fine-Tuning
Model Serving
Inference Optimization
Linux
Windows
JSON
YAML
Prompt Engineering
Multi-Agent Systems