Narvar

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery • Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features • Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly • Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams • Improve data quality and reliability through testing, observability, alerting, and validation frameworks • Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions • Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

• Design and build high-correctness Rust services that sit on critical platform paths • Introduce Rust into areas where safety, determinism, and performance are essential • Own systems from architecture → implementation → rollout → operations • Make real decisions around concurrency, async boundaries, data integrity, and failure modes • Work across service boundaries in a polyglot environment (Rust alongside other stacks) • Collaborate with product, design, and frontend engineers to build systems that are technically sound and product-aware • Improve reliability, observability, and system behavior through design, not just patching • Mentor other engineers and raise the bar on system quality and ownership

• Define and launch a unified merchant experience platform • Own merchant onboarding and self-service experiences • Ship AI-powered agents and workflows • Own permissions, entitlements, and shared services • Break big problems into small releases and iterate based on feedback • Partner across product teams to drive adoption

• Partner with product, go-to-market, and executive stakeholders — running discovery on ambiguous questions and scoping the metrics and data they actually need • Raise data trust — adding the validation, definitions, and documentation that let users rely on the numbers and our tooling • Expand and own our semantic / metrics layer — defining and maintaining metric definitions and models so analytics are consistent, trustworthy and reusable across the company • Deliver self-serve and AI-accessible analytics — curated datasets, metrics, and reporting that internal partners and our agentic / LLM querying surface can answer on their own • Ingest net new data — designing and building pipelines to bring in new sources such as GTM and product-usage data and modeling them for analytics

• Lead technical onboarding for top-tier enterprise retailers and innovative direct-to-consumer brands. • Provide consultative guidance to ensure seamless integration with Narvar’s solutions, maximizing product value and customer success. • Leverage customer business requirements and your Narvar product expertise to create clear and effective technical specifications. • Develop and validate solutions using web technologies such as APIs, JSON, CSS, HTML, and JavaScript, focusing on delivering seamless customer experiences. • Partner cross-functionally with technical and business stakeholders, both internal and external, to ensure successful project delivery. • Contribute to the growth of our global Professional Services team through the creation of documentation, process improvements, and the development of internal tools and training programs.

• Manage customer implementation projects end-to-end — from kickoff through post-go-live — ensuring alignment, tracking progress, managing risks, and driving toward key milestones. • Collaborate with cross-functional project teams, including Customer Engineers and other internal stakeholders, to ensure high-quality and on-time project delivery. • Serve as the primary point of contact for your customers throughout the implementation, building strong relationships and setting clear expectations. • Facilitate technical discussions and ensure customer requirements are accurately translated into implementation plans. • Support change management activities including scope adjustments, project timelines, and issue resolution. • Maintain up-to-date and accurate project documentation, timelines, and communications with both internal and external stakeholders. • Proactively identify risks and dependencies early in the project lifecycle, and implement mitigation plans to keep delivery on track and avoid surprises. • Participate in post-go-live handoff and knowledge-sharing sessions to ensure ongoing customer success.

• Utilize cold calling, social media, and email to generate new sales opportunities • Identify prospect's needs and suggest appropriate products/services • Build long-term trusting relationships with prospects to qualify leads as sales opportunities • Proactively seek new business opportunities in the market • Set up meetings or calls between (prospective) customers and sales executives

Role Description Narvar is looking for a high-potential B.Tech graduate with a strong foundation in Artificial Intelligence and process automation to join our team as an AI Consultant for a 12-month engagement. You will work directly on high-impact projects that use AI to automate and transform our HR and Finance functions. This is a senior internship role with real ownership and accountability. You will be expected to independently drive projects end-to-end — from understanding the problem, designing the solution, building and testing it, and handing it over to business teams. You will work closely with senior HR and Finance leaders, and your output will directly affect how hundreds of employees experience these functions. Given the compensation and duration, we expect candidates who can demonstrate either strong academic performance in AI/ML, prior projects, or hands-on experience with AI tools. Fresh thinking combined with technical seriousness is what we are looking for. Day-to-day - HR Projects - Build and maintain an AI-powered HR chatbot that answers employee questions about policies, leave, payroll, and benefits. - Design and test conversation flows for the chatbot — writing instructions, testing responses, and continuously improving accuracy. - Help digitise and organise HR policy documents so the AI can read and reference them correctly. - Analyse common employee queries to identify gaps in HR documentation and recommend content updates. - HR Operations Automation - Automate repetitive HR tasks such as leave tracking, onboarding checklists, exit checklists, and document collection using appropriate tools. - Build AI agents that monitor, update, and distribute HR dashboards to Executives and Managers — reducing manual reporting effort. - Design automated workflows that surface key HR metrics (headcount, attrition, leave utilisation) to the right stakeholders at the right time. - Performance Management Automation - Automate performance review cycle processes — reminder workflows, form collection, status tracking, and escalation nudges. - Build tools to aggregate and visualise performance data for managers, removing manual consolidation from spreadsheets. - Design AI-assisted pipelines to flag review completion rates, identify bottlenecks, and generate summary reports for HR leadership. - Talent Acquisition & Recruitment - Build dashboards and automated reports tracking talent acquisition metrics — pipeline volume, source effectiveness, time-to-offer, and offer acceptance rates. - Design AI-assisted application review workflows to help the recruitment team prioritise and triage high volumes of applications faster. - Automate candidate communication touchpoints such as acknowledgement emails, status updates, and interview scheduling reminders. - Create tools that surface hiring funnel analytics to Hiring Managers and HR leadership in real time. - Finance Projects - Automate routine finance workflows such as invoice processing, expense categorisation, and reimbursement tracking. - Build simple AI-assisted tools to flag anomalies or inconsistencies in financial data. - Create automated reports and dashboards using Google Sheets or similar tools, reducing manual data entry. - Assist in mapping existing manual finance processes and proposing AI-based alternatives. - Test and document automation tools before they are rolled out to the finance team. Qualifications - B.Tech degree in Computer Science, AI/ML, Data Science, Electronics, or related branch — 2024 or 2025 pass-out. - Demonstrable hands-on experience with AI tools — projects, hackathons, internships, or self-built tools all count. - Working knowledge of Python — able to write scripts, use libraries, and debug independently. - Strong understanding of at least one AI/ML concept: LLMs, NLP, classification, automation pipelines, or similar. - Comfortable working with Google Workspace (Docs, Sheets, Drive) and cloud-based tools. - Excellent written communication in English — you will be writing specifications, documentation, and chatbot content. - Ability to manage your own work, meet deadlines, and proactively communicate blockers. Bonus points - Experience with LLM APIs — OpenAI, Anthropic Claude, or Google Gemini. - Prior internship or freelance project involving AI, automation, or data — even if short. - Familiarity with no-code/low-code tools: Zapier, Make, Botpress, Flowise, or similar. - Basic understanding of HR or Finance workflows from coursework, projects, or prior exposure. - A GitHub profile, portfolio, or any public evidence of what you have built. - Experience with prompt engineering, RAG pipelines, or building chatbots. Benefits - Hands-on experience that most fresh graduates do not get from classroom learning alone. - How to build and deploy a working AI chatbot using real enterprise tools. - How to design automated workflows that replace manual, repetitive tasks. - How to work with HR and Finance stakeholders to understand their pain points and translate them into technical solutions. - Practical exposure to AI tools, APIs, and no-code platforms used in Indian companies. - How to document, test, and hand over a technical project to a non-technical team. - Real experience you can put on your resume and explain confidently in interviews. Company Description Narvar is on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. - Works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ other brands. - Served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages. - Pioneering the post-purchase movement means navigating into the unknown. - We are an equal-opportunity employer and value diversity at our company.

• Design and deploy machine learning algorithms for use cases spanning e-commerce, consumer trends, markets, logistics, and new products • Work on real-world consumer data for natural language processing, image classification, time series analysis, outlier detection, user modeling, etc • Work with large unstructured data • Be at the intersection of mathematics, machine learning, business, and computer science and impact millions of users through your work • Multiply the effect of your data science and data team members by building frameworks, tools, and methodologies that the entire team use • Provide thought leadership to a team through high quality write-ups, reviews, and a strong vision grounded in practical experience and a wider industry view

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery • Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features • Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly • Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams • Improve data quality and reliability through testing, observability, alerting, and validation frameworks • Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions • Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions