- Location
- Hyderabad, Telangana
- Workplace
- Remote
- Type
- Full-time
- Department
- IT
- Seniority
- Manager
- Closing date
- Today
- Source
- ApplyToJob
Description
Product Manager — AI Products
BuzzBoard · Remote (WFH) · ProductAbout BuzzBoard
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 wasn't 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, and a spec-driven development process that treats agent behavior as a first-class design artifact.
The role
This is a Product Manager role for people who build, not just specify.
Two things are equally true about how we work, and both are non-negotiable expectations of this role:
1. The products you own are agent systems, and they are non-deterministic. Your design surface is prompts, tool definitions, retrieval strategy, schema contracts, model selection, fallback behavior, and failure modes. “The model got it wrong” is a product bug you are expected to diagnose and specify a fix for — not escalate.
2. You use these models to do the product work. You will write specs, generate structured data, produce working HTML prototypes, run evaluation batches, and analyze output quality using the same models the products run on. Our specs ship as machine-readable handoff sets, and prototypes ship with live model stubs. If your definition of a spec is a Confluence page of prose, this role will be uncomfortable.
What you'll actually do
Required
BuzzBoard · Remote (WFH) · ProductAbout BuzzBoard
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 wasn't 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, and a spec-driven development process that treats agent behavior as a first-class design artifact.
The role
This is a Product Manager role for people who build, not just specify.
Two things are equally true about how we work, and both are non-negotiable expectations of this role:
1. The products you own are agent systems, and they are non-deterministic. Your design surface is prompts, tool definitions, retrieval strategy, schema contracts, model selection, fallback behavior, and failure modes. “The model got it wrong” is a product bug you are expected to diagnose and specify a fix for — not escalate.
2. You use these models to do the product work. You will write specs, generate structured data, produce working HTML prototypes, run evaluation batches, and analyze output quality using the same models the products run on. Our specs ship as machine-readable handoff sets, and prototypes ship with live model stubs. If your definition of a spec is a Confluence page of prose, this role will be uncomfortable.
What you'll actually do
- Own one or more product lines — outcomes, roadmap, sequencing, and the quality bar
- Write the spec set engineering builds from: product spec, engineering spec, model spec, and handoff readiness — decisions resolved, not deferred
- Design the agent behavior — prompt architecture, output schemas, deterministic vs. model-decided logic, guardrails, and what happens when the model is wrong or the tool call fails
- Build prototypes yourself — working HTML with live model calls, ahead of engineering commitment, so we argue about a thing instead of a document
- Define and run evaluations — build eval sets, score output batches, set ship/no-ship thresholds, and drive prompt and model iteration off measured results rather than vibes
- Make the model tradeoffs — model choice, context strategy, latency, token cost per unit of output, fine-tune vs. prompt vs. retrieval
- Work directly with engineering, GenAI, and design on daily execution; hold the line on schema and interface contracts between agents
- Interface with customers and enterprise partners, including US-based partners with real security, privacy, and compliance review processes
- Instrument and read the data — usage, quality, cost, and drift — and act on it
Required
- 4+ years in product management, ideally B2B SaaS
- Demonstrated hands-on work with LLM / agent products: prompt design, structured output, retrieval, tool/function calling, evaluation. We will ask you to walk through something you shipped in detail.
- Fluency using AI tooling in your own workflow — specs, prototypes, analysis, code reading
- Ability to read code and API contracts well enough to review a schema, follow a pipeline, and spot a bad interface
- Track record of shipping — features live, in customers' hands, with measured outcomes
- Strong written communication; you can take a technical decision and make it legible to a CEO and to an engineer in the same document
- Comfort operating with ambiguity and short cycles
- Degree in AI/ML, Engineering, CS, or a related technical field
- Direct experience with OpenAI, AWS Bedrock, Anthropic/Claude, LangChain, or equivalent orchestration stacks
- Experience with voice AI, agentic workflows, or multi-agent systems
- Experience with fine-tuning, dataset curation, or model performance analysis
- Experience working with US customers and enterprise partners
- SMB or marketing-technology domain knowledge
- Not a backlog-grooming or ticket-triage role
- Not a role where AI strategy is delegated to a data science team you email
- Not a role where a spec can end with “TBD, engineering to decide”
- Fully remote
- Genuine ownership of a product line, with the latitude to shape it
- Work at the current frontier of applied AI product development — agent systems, evals, voice, and multi-model orchestration in production
- A small, high-context team that moves quickly and argues about the work
- Real impact on the small businesses our customers serve
Skills
AWSData ScienceConfluenceCompliance
