Technical Product Manager

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🕒 June 17

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Gugu Robotics

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

🤖 Artificial Intelligence

🛍️ eCommerce

Artificial Intelligence • Robotics • eCommerce

Gugu Robotics is a pioneering startup based in West Africa, dedicated to harnessing the power of AI, robotics, and drone technology to create innovative solutions for various industries. Founded by visionary engineer Emmy Pencil, the company specializes in designing and building customized robotic systems, drones, and AI-enabled electric vehicles that address critical challenges across sectors such as agriculture, military, medical, and beyond. With a diverse team of experts, Gugu Robotics aims to redefine possibilities in automation and efficient energy solutions to shape the future of technology.

📋 Description

• Define and drive the product vision, strategy, and roadmap for GenAI solutions - with agentic AI as the primary focus - connecting AI capabilities to enterprise business outcomes • Translate enterprise problems into structured product requirements; reframe feature requests into outcome-driven priorities with explicit tradeoffs on invest in vs. defer • Balance near-term deployment milestones with long-term platform scalability and sustainability • Monitor the competitive GenAI landscape and emerging agentic patterns to inform roadmap and technology decisions • Research how enterprise users interact with AI agents and where they lose trust; frame the riskiest assumptions as testable hypotheses and de-risk them first • Design and run experiments - POCs, pilot deployments, scenario-based testing of multi-step workflows, edge cases, and failure recovery - to validate agentic solutions where non-deterministic output makes traditional QA insufficient • Distill research, experiments, and competitive intelligence into clear insights that pave the path for a successful product • Define agent behavior and prototype system prompts and tool schemas; partner with engineering on context management - summarization, working memory, and information flow across multi-step tasks • Drive multi-model architecture tradeoffs with engineering - define the quality, cost, and latency targets that determine which model serves each step in the agent workflow • Build AI prototypes to validate hypotheses; define human-in-the-loop boundaries and guardrails - when the agent acts autonomously, when it escalates, and how to handle non-deterministic output • Establish agent evaluation frameworks - task completion, reasoning quality, tool selection, failure recovery, safety - and partner with engineering on production readiness (observability, drift, responsible AI, prompt versioning) • Define success metrics at the agent level - task completion rate, cost per task (not per inference), escalation rate, time to resolution, and customer trust alongside business KPIs • Own the end-to-end product lifecycle from discovery through phased rollouts; establish the metrics framework (north star, input, guardrail metrics) and report product impact to leadership • Manage the product backlog, scope, dependencies, and risks; drive agile ceremonies and produce high-quality PRDs, product briefs, and decision logs • Evaluate technology and platform decisions from a product perspective; create deployment playbooks, reference architectures, and knowledge transfer materials so teams sustain solutions independently • Use AI to accelerate product work - research, analysis, prototyping, documentation - with judgment on when it needs human oversight; onboard rapidly to new domains and support team members across the initiative • Build trusted relationships with stakeholders and executives; serve as the go-to product advisor and primary contact for AI product direction and deployment strategy • Partner with AWS Solution Architects and account teams to align on technical approach, service selection, and go-to-market for GenAI solutions • Manage expectations on scope, timelines, and tradeoffs; facilitate decisions across competing priorities using data, alternatives, and clear rationale • Frame AI capabilities and limitations for non-technical stakeholders - manage hype cycles, set realistic expectations; surface unmet needs that deepen relationships and grow the account.

🎯 Requirements

• 8-12+ years in product management, forward deployment, or solutions engineering; must have shipped AI products from prototype through production at scale • Strong product sense - ability to identify what matters to users and the business, make prioritization calls with incomplete information, and shape products that deliver real outcomes • Deep GenAI fluency - LLMs, RAG, fine-tuning, prompt engineering, context engineering, evals - with hands-on experience building or shipping agentic systems (planning, tool use, HITL, guardrails) • Proven ability to prototype AI solutions using AI tools (Cursor, Claude, Copilot) to validate hypotheses and de-risk product decisions • Experience deploying AI solutions in enterprise environments with strong technical fluency - can read code, evaluate architectures, make product tradeoffs on technical constraints, and drive scalable deployment patterns • Exceptional communicator - clear PRDs, technical specs, and decision logs; has led AI products through full lifecycle and driven alignment with Directors, VPs, and C-level • Comfortable operating in ambiguous, fast-moving environments where the AI landscape evolves weekly • PM-level fluency across the AWS AI ecosystem - Bedrock, AgentCore, SageMaker, Strands, Kendra, OpenSearch, Lambda, Step Functions - to make informed product and architecture decisions.

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work arrangements • Professional development opportunities