Technical Product Manager

Description : We are looking for a Technical Product Manager (AI & MarTech) to drive the roadmap, experimentation, and delivery of AI-first customer engagement and marketing platforms. This role demands deep technical understanding, business acumen, and curiosity for how Agentic AI and LLMs can transform marketing workflows, decision-making, and automation. You will own the end-to-end lifecycle of AI features from ideation to production, working closely with data science, engineering, and design teams to bring intelligent, scalable features to life. If you're excited about building the next generation of AI-driven marketing tools where systems act, reason, and adapt, this is your opportunity to make an industry-level impact.Responsibilities : - Product Strategy and Vision - Define and execute the roadmap for LLM-powered applications and AI agents, aligning with market trends and user needs.- AI Innovation - Drive cutting-edge applications like chatbots, decision-support systems, and task automation.- Research and Experimentation - Collaborate with engineers and data scientists to fine-tune and deploy LLMs for real-world use cases.- AI Agent Development - Build context-aware AI agents that integrate seamlessly into user workflows.- User Experience and Adoption - Convert user pain points into intuitive, AI-driven solutions with explainable outputs.- Cross-Functional Collaboration - Work with Engineering, Data Science, and Design teams to prototype and scale LLM-based features.- Performance Metrics - Define and track KPIs such as accuracy, response time, and user engagement.- Continuous Optimisation - Enhance AI model efficiency and user satisfaction through iterative improvements.- GTM Strategy and Execution Craft the launch strategy with marketing, sales, and customer success.- Drive beta adoption, pricing pilots, and success metrics tracking.Requirements : - 8-12 + years of experience in SaaS or AI-first product management, ideally in MarTech, AdTech, or Customer Engagement platforms.- Proven success in building and scaling LLM-powered or AI-driven applications (e. g., copilots, AI agents, chatbots, recommendation systems).- Strong understanding of campaign automation, customer lifecycle marketing, and performance optimisation workflows.- Demonstrated experience working with AI engineering and data science teams on use cases involving Natural Language Processing (NLP), machine learning models, or fine-tuned LLMs.- Familiarity with Agentic AI frameworks - orchestrating agents that can plan, reason. g. n, and act autonomously (e. g., ReAct, LangGraph, CrewAI, AutoGen, or OpenAI AgentKit).- Exposure to marketing and analytics systems - from campaign management and audience segmentation to attribution and performance dashboards.- Experience collaborating with cross-functional teams (Engineering, Design, Data Science) to deliver AI-enabled MarTech features that enhance user workflows.- Understanding of prompt engineering, RAG (Retrieval-Augmented Generation), embeddings, context management, and model evaluation metrics.AI / LLM Tools and Frameworks : - OpenAI (GPT-4 GPT-4o), Anthropic Claude, Gemini, Cohere, Hugging Face Transformers.- LangChain, LlamaIndex, AutoGen, CrewAI, AgentKit, ReAct.- Vector Databases : Pinecone, Weaviate, FAISS, Chroma.- Embedding models and context management libraries.Marketing Platforms : - Ad Platforms : Meta Ads Manager, Google Ads, DV360 Affiliate Networks.- Customer Engagement Platforms : MoEngage, CleverTap, WebEngage, Braze.- Tag and Pixel Tools : Google Tag Manager, Meta Pixel.Product and Collaboration Tools : - JIRA, Confluence, Notion, Trello.- Productboard, Aha (for roadmap and backlog management).- Figma, Miro (for UX wireframing, prototyping, and workflow design).Analytics and BI Tools : - SQL (basic to intermediate proficiency).- Google Analytics, Mixpanel, Amplitude.- Tableau, Looker, Power BI (basic familiarity).- Attribution platforms like Branch, Appsflyer, etc.Technical Understanding : - REST APIs, GraphQL, Webhooks, JSON, OAuth.- Cloud Platforms : AWS, Azure, or GCP (knowledge of model deployment or hosting).- CI/CD and DevOps : GitHub, Jenkins, Docker (basic exposure to AI model deployment pipelines).- Working knowledge of Python-based AI environments (bonus if familiar with notebooks and LLM API integration). (ref:hirist.tech)