Recruiter Snapshot
Fast clarity for AI product hiring teams.
The shortest version: I am targeting full-time AI PM and Technical PM roles where product judgment, technical fluency, and founder ownership matter.
Target roles
AI PM / Technical PM.
Best fit for AI-native products, B2B SaaS, automation platforms, internal copilots, marketplace workflows, and 0-to-1 product teams.
Why hire meWork mode
Open worldwide.
Based in Tiruppur, India; lived and worked in Houston, Columbus, Detroit and Austin from 2017 to 2022. Open to remote, hybrid, on-site, relocation, and international assignments.
Open JD MatcherOperating proof
Founder-level ownership.
At ZAS Digital, I helped ship 15+ products across 4 countries, spanning healthcare, energy, retail, logistics, EdTech, F&B, and automotive.
View proof of workContact
Ready for screening.
Email me, request the latest resume, or use the JD matcher for a fast role-fit read before reaching out.
Request resumeFounder-Operator Proof
My strongest proof is founder-built product work.
Maanavar, EduHire, Vinaadi, and Eflex show the arc recruiters should care about: ground-up ownership, recent AI product execution, and long-running technology partnership under real constraints.
Maanavar LMS
Owned R&D, product, UX, development, launch, and post-COVID shutdown decisions. Shelved, but shaped my entrepreneurship and product leadership.
EduHire
Recent teacher-school hiring product using deterministic scoring plus AI-assisted screening, profiles, resumes, and cover letters.
Vinaadi AI
Recent Tamil-first astrology platform combining calculation engines, domain rules, AI guidance, planning, cautions, and bilingual UX.
Eflex
Long-standing clean energy partnership where we act as technology partners for research, development, product evolution, and AI capability expansion.
2024–2026 · What separates me
Most product people are still reading about the AI era. I am already shipping in it. Claude, RAG, MCP, and agentic workflows are embedded in every sprint — not explored in slide decks.
How I Build AI Products
A product journey built for practical AI adoption.
Each stage is designed to reduce risk, clarify value, and keep AI tied to product outcomes instead of hype.
Start with the problem, not the model.
- Identify user pain and business stakes
- Check data readiness and workflow constraints
- Define success metrics before architecture
Shape trust and fallback behavior early.
- Map user journeys and intervention points
- Define human review and confidence thresholds
- Turn ambiguity into MVP scope and PRD clarity
Translate product intent into working systems.
- Prototype RAG, agents, and tool-calling flows
- Integrate APIs, context sources, and automation paths
- Design around real latency and data constraints
Measure quality where users feel risk.
- Check accuracy, hallucination, and trust signals
- Validate task completion and business impact
- Expose failure patterns before scale
Ship with instrumentation and feedback loops.
- Release MVPs with clear usage tracking
- Align stakeholders around learning goals
- Turn pilot behavior into roadmap decisions
Use product signals to refine the system.
- Improve retention, automation rate, and UX confidence
- Prioritize the next bottleneck, not random features
- Iterate toward durable product value
Proof of Work
Founder products first, client proof after.
The first cases show end-to-end product ownership and recent AI execution. Later cases add long-running client and domain proof.
Birth chart
Rules engine
AI guidance
Plans and cautions
Vinaadi AI astrology platform
A recent Tamil-first AI product where deterministic Vedic astrology calculations, cultural localization, and AI guidance work together to make a complex traditional domain usable for modern users.
ProblemTraditional Vedic astrology was inaccessible to modern Tamil users: jargon-heavy, inconsistent, and untailored to Tamil Nadu's practices and language.
Product decisionBuilt a reliable calculation engine (Swiss Ephemeris, Dasha rules, Gochar transits) and an AI guidance layer that predicts, plans, cautions, and instructs beyond simple chat.
OutcomeBilingual platform with public tools, authenticated dashboard, family vault, daily guidance engine, and AI chat. Founder-owned from concept to live product.
Recruiter takeawayShows recent AI product ownership where I shaped domain rules, system behavior, UX, and user trust from the ground up.
Teacher profile
Structured score
AI HR assist
EduHire teacher recruitment platform
A recent Tamil Nadu teacher-school hiring product where structured scoring and AI assistance help schools screen faster while helping teachers present stronger profiles.
ProblemSchools in Tamil Nadu spent weeks manually screening teachers with no structured way to match subject, board, grade, location, salary, and experience.
Product decisionBuilt a deterministic scoring engine (subject 40%, location 20%, board 20%, salary 10%, experience 10%) and layered AI for screening narratives, teacher profiles, resumes, and cover letters.
OutcomeFull-stack platform with role-based dashboards, WebRTC live interviews, subscription plans, and a managed recruitment service for schools and teachers.
Hiring relevanceShows marketplace thinking, explainable matching, and AI-assisted HR workflows where automation supports human judgment.
R&D
UX + development
Launch + learning
Maanavar school and college LMS
An EdTech LMS I worked on from R&D to UX, development, launch, institutional deployment, and shutdown decisions. It was shelved, but it became a major entrepreneurship and leadership foundation.
ProblemSchools and engineering colleges had no digital learning infrastructure as COVID forced an abrupt shift to online education.
Product decisionBuilt a full-stack LMS with social media-inspired UX, courses, progress tracking, assessments, gamification, SIS/Ed-Fi thinking, and onboarding workflows.
OutcomeDeployed in a few schools and engineering colleges, then shelved after institutions lost interest in online learning post-COVID.
Hiring relevanceShows end-to-end founder ownership: research, product, UX, development, launch, customer learning, and the judgment to stop when the market signal changed.
Enterprise story
AI chapters
Interactive demo
Enmovil AI interactive experience
A recent client project where I used AI coding agents to accelerate delivery while translating a complex logistics AI story into a clear enterprise product experience.
ProblemStatic marketing pages could not explain six interconnected AI product chapters for enterprise prospects.
BuiltScroll-driven experience with 17 interactive hotspots across the logistics AI value chain.
OutcomeShipped in two weeks versus an eight-week original estimate by combining clear product scope with AI coding agents.
Hiring relevanceShows speed, product storytelling, and hands-on AI-assisted execution on a real client engagement.
Household data
Usage visibility
Energy savings
Eflex clean energy platform
A long-running consumer energy product where product thinking, behavior change, and operational clarity built lasting traction, with AI added when the product was ready for predictive insights.
ProblemUsers had rising utility costs but no clear view of what to change or when.
Product decisionBuilt a mobile-first energy experience with usage visibility, alerts, low-friction onboarding, and later AI-driven savings recommendations.
Outcome3k+ users, average 25% savings, multi-year partnership, and ongoing AI capability expansion.
Hiring relevanceShows long-term product ownership and the ability to evolve a mature product with AI at the right stage.
OCR scan
Classification
Real-time alert
Recall management system
Compliance workflow redesign using OCR and machine learning to remove manual review bottlenecks while preserving operational oversight.
ProblemDocument review cycles took days and slowed operational response.
BuiltOCR classification pipeline with real-time alerting for standard cases and review paths for exceptions.
Outcome98% classification accuracy and review time reduced from days to minutes.
Hiring relevanceDemonstrates practical automation design under real-world operational constraints.
AI Systems I Can Productize
I think in systems, boundaries, and user outcomes.
These are not just technical patterns. They are product surfaces that need trust, control, and measurable value.
RAG systems
User query
Retriever
Pinecone / Weaviate
Context
LLM answer
I focus on retrieval quality, answer reliability, and the UX expectations around grounded responses.
AI agents
Goal
Agent plan
Tool call
Result
Action
I design agent workflows with boundaries, approvals, and business metrics instead of open-ended autonomy.
Human-in-the-loop AI
AI suggestion
Confidence check
Human review
Approved action
I use review loops to build trust, control, and safer adoption where automation should not be absolute.
AI evaluation
Test set
Output
Score
Error pattern
Product fix
I evaluate AI like a product problem: accuracy, latency, acceptance, and business relevance all matter.
How I Think About AI Products
Frameworks I apply before writing a single line of PRD.
These are the mental models I use to kill bad AI ideas early, define the right architecture, and protect users when the model fails.
Framework 01
RAG vs Agent: pick the right architecture first.
RAG is the right call when you need grounded, accurate answers from your own data — support bots, knowledge bases, document Q&A. Agents are right when you need multi-step autonomous action — workflow automation, dynamic API orchestration, real-time decision loops. Mixing them without intent creates unpredictable systems users cannot trust.
Framework 02
Define the human-in-the-loop boundary before writing the PRD.
I establish exactly where AI assists and where humans decide before a sprint starts. AI handles scaffolding, classification, test generation, and documentation. Humans decide architecture, security, UX choices, final QA, and product direction. This boundary belongs in the PRD — not left to engineers to guess mid-sprint.
Framework 03
Five questions that kill 70% of bad AI product ideas.
- Is this a real user need or an AI use case seeking a problem?
- What is the data quality, and can the model work with what exists today?
- Can the LLM hallucinate into a critical user path?
- What is the fallback when AI fails or returns low confidence?
- Is the latency acceptable for this specific UX context?
Try My AI Product Thinking
Three live tools, framed like product demos.
Recruiters can check fit, hiring managers can see how I scope product work, and anyone can ask about my experience directly.
Ask Senthil
Experience, strengths, availability
Ask About My Experience
Project Scoper
Idea to MVP plan
Scope an MVP
JD Matcher
Role fit and hiring signal
Try JD Matcher
Ask me anything about hiring, AI product work, or fit.
Use this to quickly understand my background, working style, and where I fit best.
SK
Senthil's AI
Live via protected Groq proxy
Free shared access — 10 requests per hour. Add your own key for unlimited.
Stored only in your browser · sent only to Groq's API · never to this site.
How to get a free Groq key
- Visit console.groq.com and create a free account
- Go to API Keys and click Create API Key
- Copy the key (starts with
gsk_) and paste it above
Turn an idea into a realistic MVP plan.
This mirrors how I would structure a short AI discovery sprint.
Structured output will appear here.
Expect a problem read, stack recommendation, three-week MVP plan, top risks, and an honest recommendation.
Paste a job description and get a direct fit analysis.
Built for recruiters who want a fast view of strengths, gaps, and where I add more than the brief asks.
Fit analysis will appear here.
You will get a fit score, strong matches, honest gaps, and a recommendation on whether to reach out.
Capability Map
A recruiter-readable map of what I can lead.
Organized around role fit, product judgment, technical fluency, and domain proof.
AI product strategy
- Use-case discovery
- RAG workflows
- Agentic automation
- AI evaluation
- Human-in-the-loop UX
Product management
- PRD writing
- Roadmapping
- MVP definition
- Stakeholder alignment
- Agile delivery
Technical fluency
- APIs, tool calling, MCP servers
- Pinecone and Weaviate (vector DBs)
- OpenAI and Anthropic Claude APIs
- WebRTC and real-time systems
- Stripe, Razorpay, PayPal (PCI-DSS)
Growth and metrics
- Activation
- Retention
- Funnel analysis
- PLG thinking
- Experimentation
UX and adoption
- User journey mapping
- Onboarding flows
- Trust and safety patterns
- Fallback design
- Workflow design
Domain proof
- Healthcare patient portals
- Clean energy consumer products
- Retail and e-commerce
- Logistics and maritime operations
- EdTech and hiring workflows
Credentials
- University of Houston CS
- Engineering in Computer Science
- Google Project Management
- Sitecore Certified Developer
Experience Timeline
Engineer to founder to AI product leader.
A condensed timeline focused on impact, decision scope, and the kinds of environments I have operated in.
Co-Founder and Head of Product, ZAS Digital
2019 to presentLead product strategy and delivery across AI, energy, logistics, retail, healthcare-adjacent, and EdTech engagements.
- 15+ products shipped across 4 countries through ZAS Digital
- EdTech, Healthcare, Clean Energy, Retail, Logistics, F&B and Automotive exposure
- AI-accelerated delivery used under senior product and engineering review
- Current engagement: Vinaadi (Apr 2026–present)
Founder and Product Manager, Maanavar
Dec 2019 to Apr 2022Built a school and college LMS from scratch, deployed in a few institutions, shelved post-COVID as schools lost interest in online learning.
- Full-stack 0-to-1 ownership across product, architecture, and delivery
- Learned firsthand when market timing and user behavior override product quality
Research Consultant, TARDIS Corporation
Jan 2019 to Jun 2019Worked on enterprise data models and research-oriented solution design for decision support systems.
Sitecore Consultant, Element Blue
Sep 2018 to Dec 2018Improved content operations and CMS workflow automation for enterprise clients.
- 85% reduction in operational time on key workflows
Software Engineer, Mindtree on P&G account
Oct 2014 to Jun 2016Built scalable digital experience components across 20+ localized product sites in a global enterprise environment.
Why Hire Me
Built for AI product roles that need more than backlog management.
The through-line across my work is systems thinking, execution discipline, and practical product judgment.
I bridge product and AI execution.
I can work across design, engineering, business, and users to turn an AI concept into a usable workflow.
I think in systems, not isolated features.
I care about data quality, evaluation, trust, adoption, and downstream operations, not just feature launches.
I move quickly from idea to MVP.
I scope fast, prototype clearly, and prioritize the shortest path to a useful product signal.
I optimize for practical adoption.
My bias is toward products that teams can trust, measure, and improve in production.
Have an AI PM role in mind?
Match My Profile to JDWhat People Say
Feedback from founders, PMs, and technical leaders.
From EdTech to Energy to SaaS — people who have worked alongside me or hired me through ZAS Digital.
"Senthil has an unusually rare skill — thinks like a founder, executes like an engineer. He shapes ambiguity into buildable specs without losing the user in the process."
"AI-augmented delivery at ZAS is real, not marketing. Shipped a full MVP in 3 weeks when other agencies quoted 8. Quality held across the board."
"Brings genuine business instinct into every technical decision. Understands bottom-line impact and pushes back when items don't move the needle."
Get in touch
Hiring for an AI product role?
Send the role, JD, or interview context. I am open to full-time AI Product Manager, Technical Product Manager, Product Owner, and AI-native product leadership roles.
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