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karti479/README.md

Black White Modern Minimalist Data Analyst LinkedIn Banner (2)

A passionate Product Manager from India

karti479

karti479

# Kartik Singh

AI Product Manager | Platform Product Manager | Technical Product Manager

I build and scale technical products across AI, developer platforms, cloud infrastructure, DevOps, security, and enterprise SaaS. My work sits at the intersection of product strategy, engineering execution, platform adoption, governance, and customer outcomes.

I have moved across the full product lifecycle: discovery, product strategy, roadmap planning, PRDs, user stories, architecture discussions, execution, launch, adoption, KPI tracking, and continuous improvement. I bring a strong technical foundation from DevOps, SRE, automation, QA, APIs, cloud, Kubernetes, CI/CD, and data-driven product management.

LinkedIn | Medium | Portfolio

Product Focus

Area What I Work On
AI Product Management AI-assisted workflows, product copilots, LLM use cases, agentic automation, prompt design, evaluation loops, human-in-the-loop approval, AI governance, adoption metrics
Platform Product Management Developer experience, internal platforms, CI/CD, Kubernetes, cloud infrastructure, self-service onboarding, reliability, observability, security, compliance
Technical Product Management APIs, integrations, architecture tradeoffs, technical discovery, system constraints, engineering planning, dependency management, release readiness
Traditional Product Management Product vision, roadmap, prioritization, discovery, personas, journey maps, PRDs, acceptance criteria, experimentation, GTM, stakeholder alignment

My Product Operating Style

  • Start with the user problem, business outcome, and measurable success metric.
  • Translate ambiguous business asks into product scope, technical requirements, and execution plans.
  • Build platforms that reduce friction for developers, operators, and enterprise users.
  • Use data, customer feedback, support signals, and platform telemetry to prioritize.
  • Keep governance, security, compliance, and reliability visible from discovery through launch.
  • Drive adoption through clear documentation, launch communication, enablement, and feedback loops.

AI PM Skill Map

AI strategy and discovery

  • Identify high-value AI use cases from repeated workflows, manual decisions, support load, and operational bottlenecks.
  • Define AI product value propositions, user journeys, risks, trust boundaries, and adoption metrics.
  • Convert vague AI ideas into concrete MVPs, experiments, evaluation plans, and rollout paths.

LLM and agent product design

  • Design AI assistants, copilots, workflow agents, and human-in-the-loop approval systems.
  • Define prompts, context sources, tool permissions, memory boundaries, and escalation rules.
  • Separate safe automation from actions that require human approval.

Evaluation and governance

  • Create quality metrics for AI outputs: accuracy, usefulness, completeness, tone, policy safety, latency, and cost.
  • Plan feedback loops, red-team checks, regression tests, and release gates for AI features.
  • Handle responsible AI concerns: privacy, auditability, hallucination risk, explainability, access control, and compliance.

AI product execution

  • Partner with engineering on APIs, embeddings, retrieval, orchestration, model selection, tool calling, and observability.
  • Launch AI products with onboarding, documentation, support paths, dashboards, and adoption reporting.

Platform PM and Technical PM Skill Map

Platform strategy

  • Developer platforms, internal tools, infrastructure products, CI/CD modernization, cloud adoption, and self-service workflows.
  • Platform adoption funnels, migration planning, enablement, roadmaps, operating models, and success metrics.

Technical depth

  • APIs, REST services, Kubernetes, Docker, Jenkins, GitHub, CI/CD, cloud services, observability, secrets management, and release automation.
  • Architecture tradeoffs, non-functional requirements, scalability, reliability, security, compliance, and operational readiness.

Execution

  • Jira/Confluence operating model, epics, stories, acceptance criteria, backlog refinement, sprint planning, dependency tracking, release readiness, and launch communication.
  • Cross-functional leadership with engineering, design, QA, security, operations, support, leadership, and external partners.

Traditional PM Skills

  • Product vision and strategy
  • Product discovery and customer research
  • Personas, user journeys, and problem framing
  • PRDs, epics, user stories, and acceptance criteria
  • Prioritization frameworks and roadmap planning
  • Agile delivery, backlog management, sprint planning, and release planning
  • GTM planning, launch communication, FAQs, enablement, and adoption tracking
  • Metrics, dashboards, funnel analysis, A/B testing, and KPI ownership
  • Stakeholder management, executive communication, and decision facilitation
  • Compliance, risk management, audit readiness, and governance workflows

Tools and Technologies

Product and analytics: Jira, Confluence, Miro, Figma, Balsamiq, Whimsical, Mixpanel, Google Analytics, Amplitude, Tableau, Power BI

AI and automation: LLM workflows, prompt design, AI agents, evaluation design, Python automation, workflow orchestration

Platform and engineering: GitHub, Jenkins, CI/CD, Docker, Kubernetes, REST APIs, HashiCorp Vault, AWS, GCP, Linux, SQL, Postman

Quality and observability: Selenium, Appium, Pytest, Robot Framework, TestNG, Datadog, New Relic, Grafana

Products and Experiments

  • StaticKuber - Kubernetes manifest validation for faster developer feedback.
  • GapJap - Communication follow-up assistant for stalled conversations and response gaps.
  • SafeWall - Security-focused web protection concept.
  • Auto-HPA - Kubernetes autoscaling assistant based on workload metrics.

Experience Themes

  • Enterprise SaaS and B2B product management
  • Banking, payments, fintech, investment, and regulated product domains
  • Developer productivity, platform engineering, DevOps, and cloud infrastructure
  • AI-enabled customer experience and operational automation
  • Product-led adoption, KPI improvement, and stakeholder alignment

What I Am Building Toward

I am focused on the next generation of AI-native product management and platform products: assistants that help teams make better decisions, developer platforms that reduce operational friction, and governance models that let enterprises adopt AI safely.

My goal is to build products that are technically credible, easy to adopt, measurable, and trusted by both users and engineering teams.

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