{"id":3290585,"url":"https://alion.io/job/buzzboard-product-manager-ai-products","title":"Product Manager — AI Products","company":{"id":2965912,"name":"BuzzBoard","domain":"buzzboard.ai","url":"https://alion.io/company/buzzboard","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Product","role_family":"Product","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Claude","optional":false},{"name":"Confluence","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-10-07T16:45:33Z","employer_posted_date":null,"last_verified_at":"2026-10-07T16:45:33Z","board_verified":false,"closed_at":null,"days_open":26,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":26},"description":"Product Manager — AI Products\nBuzzBoard · Remote (WFH) · ProductAbout BuzzBoard\nBuzzBoard 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.\nOur platform is built on frontier models end to end. Not AI features bolted onto a legacy product — the products are agent systems.\nOur 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.\nThe role\nThis is a Product Manager role for people who build, not just specify.\nTwo things are equally true about how we work, and both are non-negotiable expectations of this role:\n1. 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.\n2. 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.\nWhat you'll actually do\n\nOwn one or more product lines — outcomes, roadmap, sequencing, and the quality bar\n\nWrite the spec set engineering builds from: product spec, engineering spec, model spec, and handoff readiness — decisions resolved, not deferred\n\nDesign 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\n\nBuild prototypes yourself — working HTML with live model calls, ahead of engineering commitment, so we argue about a thing instead of a document\n\nDefine 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\n\nMake the model tradeoffs — model choice, context strategy, latency, token cost per unit of output, fine-tune vs. prompt vs. retrieval\n\nWork directly with engineering, GenAI, and design on daily execution; hold the line on schema and interface contracts between agents\n\nInterface with customers and enterprise partners, including US-based partners with real security, privacy, and compliance review processes\n\nInstrument and read the data — usage, quality, cost, and drift — and act on it\n\nWhat we're looking for\nRequired\n4+ years in product management, ideally B2B SaaS\n\nDemonstrated 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.\n\nFluency using AI tooling in your own workflow — specs, prototypes, analysis, code reading\n\nAbility to read code and API contracts well enough to review a schema, follow a pipeline, and spot a bad interface\n\nTrack record of shipping — features live, in customers' hands, with measured outcomes\n\nStrong written communication; you can take a technical decision and make it legible to a CEO and to an engineer in the same document\n\nComfort operating with ambiguity and short cycles\n\nPreferred\nDegree in AI/ML, Engineering, CS, or a related technical field\n\nDirect experience with OpenAI, AWS Bedrock, Anthropic/Claude, LangChain, or equivalent orchestration stacks\n\nExperience with voice AI, agentic workflows, or multi-agent systems\n\nExperience with fine-tuning, dataset curation, or model performance analysis\n\nExperience working with US customers and enterprise partners\n\nSMB or marketing-technology domain knowledge\n\nWhat this role is not\nNot a backlog-grooming or ticket-triage role\n\nNot a role where AI strategy is delegated to a data science team you email\n\nNot a role where a spec can end with “TBD, engineering to decide”\n\nWhat we offer\nFully remote\n\nGenuine ownership of a product line, with the latitude to shape it\n\nWork at the current frontier of applied AI product development — agent systems, evals, voice, and multi-model orchestration in production\n\nA small, high-context team that moves quickly and argues about the work\n\nReal impact on the small businesses our customers serve\n\nApply if you're a product manager who builds with these models daily and wants to own products made of them.\nOriginally posted on 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