Home

Rajender - AI Product Manager

[email protected]

Location: Frisco, Texas, USA

Relocation: Yes

Visa: STEM OPT

Resume file: Rajender_AI_PM_Resume_v5_1777296926408.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

Rajender Naik G AI Product Manager Texas, USA | (551)208-2001| [email protected] | LinkedIn | GitHub PROFESSIONAL SUMMARY AI Product Manager with a technical foundation in AI/ML engineering and a Master s in Business Analytics. Over the past two years I ve owned product strategy, roadmap, and cross-functional delivery for two LLM-powered B2B products at Vitharka running discovery, writing PRDs, defining success metrics, and partnering with engineering through ship. Before that I shipped 2+ years of production software as an AI/ML engineer, which means I can talk to engineers without a translator and debate trade-offs in the same language they use. I understand how AI systems work at the code level and how people actually use them at the human level. Most PMs come from one side and learn to work with the other. I ve lived in both. CORE COMPETENCIES Product Strategy & Roadmap: Product Discovery, PRD Writing, OKR/KPI Definition, Prioritization Frameworks (RICE, MoSCoW), Competitive Analysis AI/ML Product: LLM Product Design, RAG Architectures, Prompt Engineering, AI Workflow Orchestration, Model Evaluation Execution & Collaboration: Agile/Scrum, Cross-functional Stakeholder Management, User Research, A/B Testing, GTM Strategy Technical Stack: Python, TypeScript, FastAPI, React, AWS, LangChain, OpenAI APIs, PostgreSQL, Docker, CI/CD PROFESSIONAL EXPERIENCE Software Engineer, AI/ML | Vitharka Inc, Texas, USA Mar 2025 Present Moved into hands-on engineering to build the features I d previously scoped. Shipped LLM-powered product features using OpenAI APIs and RAG pipelines in production. Designed RESTful and GraphQL APIs in FastAPI and Spring Boot with JWT/RBAC for auth. These APIs handle real-time data exchange across a distributed set of cloud-native microservices and stay reliable under load. Worked across the stack on React, Angular, and Python applications with a modular component structure. Feature delivery cycles got 30% faster because the architecture made it easy to build new things without breaking existing ones. Set up CI/CD pipelines using GitHub Actions and Docker, deployed on AWS. Release cycles came down 20% with consistent production stability. Wrote C++ modules for the most compute-intensive parts of the backend. Service latency dropped 18% and request throughput went up 22% under peak traffic. AI Product Manager | Vitharka Inc, Client: FNA-APP, Texas, USA Jan 2024 Feb 2025 Owned product strategy and roadmap for two LLM-powered B2B products, partnering with engineering, design, and business stakeholders from discovery through launch. Shipped features that moved core usage and quality metrics in every quarter of tenure. Ran structured user discovery with end users and internal stakeholders to identify workflow bottlenecks. Synthesized findings into PRDs, user stories, and acceptance criteria that engineering could build against without ambiguity. Defined product success metrics for probabilistic AI systems response relevance, hallucination rate, task completion, and user trust signals and drove a 35% lift in response relevance and a measurable reduction in hallucinations across user-facing AI experiences. Prioritized the roadmap using RICE scoring and trade-off analysis across reliability, feature breadth, and latency. Made and defended the calls on what shipped now, what shipped next, and what got cut. Led A/B tests on prompt structure, UI affordances, and onboarding flow. Instrumented the full funnel, read the data honestly, and killed experiments that didn t move the needle rather than forcing a narrative. Partnered with engineering on RAG architecture trade-offs chunking strategy, retrieval quality, context window management translating technical constraints into product decisions the broader team could weigh in on. Drove Agile/Scrum ceremonies across a cross-functional team, kept a multi-sprint roadmap credible, and held 99.5%+ uptime on release commitments by pairing release planning with realistic capacity. Ran competitive analysis on adjacent LLM products and fed insights back into positioning, pricing conversations, and feature prioritization. Software Engineer | Foinix Software Services Pvt Ltd, India May 2021 Dec 2022 Wrote Python REST APIs that connected to React frontends across several internal product tools. Focused on making the data flows predictable and the error handling solid so the business workflows actually held up in production. Built data ingestion pipelines that pulled from CSV files, Excel sheets, and relational databases, validated everything on the way in, and logged failures clearly. Took recurring manual processing work that was eating 30% of the team s time and made it automatic. Fixed slow database queries through indexing and query restructuring. Execution speed improved 20% across the board. Automated the weekly and monthly reporting pipeline with Python scripts and SQL procedures. Report generation time dropped 40%. PROJECTS Agentic AI Support Ticket System | Multi-Node LangGraph Agent Built a multi-node agentic AI system using LangGraph with specialized nodes for intake classification, priority routing, solution generation, and escalation handoff. Handles the full ticket lifecycle autonomously. Wired in human-in-the-loop checkpoints at the escalation node so the system pauses and flags ambiguous cases for human review keeping automation reliable without removing human judgment where it matters. UrbanCountry Realty CRM Dashboard | PropTech Product and UI Built a CRM dashboard for a realty and property management company using React and Laravel. Handles listing management, client tracking, and broker analytics. Live production tool used daily by the team. University Churn Prediction Platform | Saint Peter s University, MS Capstone Built a student churn prediction platform in Python and SQL. Engineered features from enrollment and engagement data, tuned a scikit-learn model through several iterations, and got early churn identification accuracy up 18% over baseline. Enterprise Data Validation Framework | Johnson & Johnson, USA Wrote Python-based data validation and ingestion workflows using JSON Schema to enforce data quality standards across large datasets. Deployed as a gating layer before data reached downstream systems. EDUCATION Master of Science in Business Analytics | Saint Peter s University, USA November 2024 Relevant coursework: Data-Driven Product Strategy, Machine Learning, Statistical Modeling, Business Intelligence, Analytics for Decision-Making Keywords: cplusplus continuous integration continuous deployment artificial intelligence machine learning user interface information technology microsoft mississippi