AI Product Data Scientist

Job Description

As an AI Product Data Scientist at our company, you will play a crucial role in designing and scaling intelligent autonomous systems across enterprise products. Your expertise in Agentic AI systems and product ecosystem architecture will be key in bridging AI research, data science, and product engineering. Here is a summary of your responsibilities:- Design and develop agentic AI systems including multi-agent workflows, autonomous decision systems, and orchestration frameworks.- Build AI-driven product features across end-to-end product ecosystems such as APIs, services, data platforms, and automation pipelines.- Collaborate closely with product managers, engineering teams, and stakeholders to translate business requirements into AI solutions.- Develop ML/LLM models, embeddings, vector search, recommendation systems, and predictive analytics pipelines.- Architect RAG pipelines, AI copilots, enterprise automation workflows, and AI-first product integrations.- Ensure that AI systems are scalable, reliable, explainable, and production-ready.- Drive experimentation, model evaluation, and continuous improvement in AI products.To excel in this role, you should possess the following skills and qualifications:- Strong experience in Data Science, ML, and AI product development.- Expertise in Agentic AI frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.- Experience with LLMs, RAG, embeddings, and vector databases like Pinecone, FAISS, Weaviate, etc.- Solid programming skills in Python, including FastAPI, ML libraries, and data pipelines.- Experience in designing AI product ecosystems involving microservices, APIs, event-driven systems, and cloud-native architectures.- Knowledge of cloud platforms like AWS, GCP, Azure, and MLOps pipelines.- Strong understanding of product metrics, experimentation, and AI-driven product strategy.If you are passionate about leveraging AI technologies to drive innovation and growth, we encourage you to share your resume with us. As an AI Product Data Scientist at our company, you will play a crucial role in designing and scaling intelligent autonomous systems across enterprise products. Your expertise in Agentic AI systems and product ecosystem architecture will be key in bridging AI research, data science, and product engineering. Here is a summary of your responsibilities:- Design and develop agentic AI systems including multi-agent workflows, autonomous decision systems, and orchestration frameworks.- Build AI-driven product features across end-to-end product ecosystems such as APIs, services, data platforms, and automation pipelines.- Collaborate closely with product managers, engineering teams, and stakeholders to translate business requirements into AI solutions.- Develop ML/LLM models, embeddings, vector search, recommendation systems, and predictive analytics pipelines.- Architect RAG pipelines, AI copilots, enterprise automation workflows, and AI-first product integrations.- Ensure that AI systems are scalable, reliable, explainable, and production-ready.- Drive experimentation, model evaluation, and continuous improvement in AI products.To excel in this role, you should possess the following skills and qualifications:- Strong experience in Data Science, ML, and AI product development.- Expertise in Agentic AI frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.- Experience with LLMs, RAG, embeddings, and vector databases like Pinecone, FAISS, Weaviate, etc.- Solid programming skills in Python, including FastAPI, ML libraries, and data pipelines.- Experience in designing AI product ecosystems involving microservices, APIs, event-driven systems, and cloud-native architectures.- Knowledge of cloud platforms like AWS, GCP, Azure, and MLOps pipelines.- Strong understanding of product metrics, experimentation, and AI-driven product strategy.If you are passionate about leveraging AI technologies to drive innovation and growth, we encourage you to share your resume with us.

Keyskills :
Data SciencePythonAI product developmentAgentic AI frameworksLLMsRAGembeddingsvector databasescloud platforms