Title: Digital Shelf Content Writer – India (Pune preferred) Company: Genrise.ai — software start-up that automates digital shelf optimization for US consumer brands Type: Full-time · Remote/Hybrid (India) · Pune preferred Start: ASAP Experience: 1–2 years What you’ll do Write and optimize product titles, bullets, descriptions, and backend keywords for Amazon, Walmart, Target, Sam’s Club and other retailers. Use Helium 10 and Jungle Scout to research and map high-intent keywords. Match brand tone/voice and ensure claims/compliance for Food & Beverage, Beauty, and Pet Care. What you bring 1–2 years of content writing experience Working knowledge of marketplace content ranking mechanics (relevance, keyword indexing, content quality). Strong US/UK English, crisp copy, and attention to detail. Nice to have Exposure to Marketplace/ ecommerce content writing KPIs you’ll influence Share of Voice (SOV) . (Plus) organic ranking and conversion impact on PDPs. Compensation ₹20,000–₹25,000 per month. How we hire CV screen → quick test task → interview → offer. Reports to the CEO. Apply Use LinkedIn Easy Apply and include 2–3 links you’ve written/optimized.
Company Description Genrise.ai is an ecommerce content agent designed to address the content needs of brands on various marketplaces like Amazon, Walmart, and Target. By identifying content gaps, creating tailored and high-performing product copy, Genrise.ai accelerates the content creation process, delivering results 10x faster without any manual effort. We are now live on #ProductHunt! About the role Build and ship stateful, agentic AI workflows using LangGraph (and LangChain where useful). You’ll turn product ideas into robust, LLM-powered features with careful context + memory management , strong Python engineering, and a bias for measurable performance. What you’ll do Design, build, and deploy agentic pipelines with LangGraph (multi-step tools, branching, retries, guards). Orchestrate LLM calls, state management , and long-context strategies (summarization, retrieval, windowing). Integrate APIs, vector databases, and observability to ensure reliability, latency, and cost control. Work with reasoning models and implement tool use, function calling, and evaluation loops. Contribute to fine-tuning/exposure workflows (data prep, small adapters/LoRA, evals, safety checks). Optimize prompts, caching, and throughput; write clean, testable Python. What you’ll bring Strong Python plus hands-on LangGraph/LangChain experience. Deep understanding of memory management and context control for LLM apps. Solid grasp of LLM orchestration, RAG, embeddings, and vector stores. Experience integrating external tools/APIs and shipping to production. Pragmatic problem-solver with metrics-driven mindset (latency, quality, cost). Nice to have Experience with eval frameworks, tracing/telemetry, streaming UIs, and cloud deployment (Docker, serverless, Kubernetes). Prior work with reasoning-focused or multi-agent systems and lightweight fine-tuning .
Company Description Genrise.ai is an ecommerce content agent designed to address the content needs of brands on various marketplaces like Amazon, Walmart, and Target. By identifying content gaps, creating tailored and high-performing product copy, Genrise.ai accelerates the content creation process, delivering results 10x faster without any manual effort. We are now live on #ProductHunt! About the role Build and ship stateful, agentic AI workflows using LangGraph (and LangChain where useful). Youll turn product ideas into robust, LLM-powered features with careful context + memory management , strong Python engineering, and a bias for measurable performance. What youll do Design, build, and deploy agentic pipelines with LangGraph (multi-step tools, branching, retries, guards). Orchestrate LLM calls, state management , and long-context strategies (summarization, retrieval, windowing). Integrate APIs, vector databases, and observability to ensure reliability, latency, and cost control. Work with reasoning models and implement tool use, function calling, and evaluation loops. Contribute to fine-tuning/exposure workflows (data prep, small adapters/LoRA, evals, safety checks). Optimize prompts, caching, and throughput; write clean, testable Python. What youll bring Strong Python plus hands-on LangGraph/LangChain experience. Deep understanding of memory management and context control for LLM apps. Solid grasp of LLM orchestration, RAG, embeddings, and vector stores. Experience integrating external tools/APIs and shipping to production. Pragmatic problem-solver with metrics-driven mindset (latency, quality, cost). Nice to have Experience with eval frameworks, tracing/telemetry, streaming UIs, and cloud deployment (Docker, serverless, Kubernetes). Prior work with reasoning-focused or multi-agent systems and lightweight fine-tuning . Show more Show less