Senior AI Engineer Data Science & Generative AI

As a Senior AI Engineer specializing in Data Science & Generative AI, your mission is to build and productionize intelligent AI systems by combining deep data science expertise with engineering rigor to deliver scalable Gen AI solutions. Key Responsibilities: - Design and build end-to-end Gen AI and ML pipelines, from data exploration to production deployment - Develop agentic AI systems such as RAG, tool orchestration, planning/execution flows - Build retrieval services, embedding pipelines, and model-routing infrastructure - Train, fine-tune, and evaluate LLMs and ML models; implement monitoring and cost-control frameworks - Translate data science research into robust, production-grade services - Collaborate with Engineering and Product teams; contribute to AI architecture decisions Qualifications Required: - 5+ years of experience in data science, ML engineering, or AI development - Strong proficiency in Python with solid software engineering practices - Hands-on experience with Gen AI technologies such as LLMs, prompt engineering, RAG, embeddings, and agents - Deep analytical skills to explore, understand, and interpret complex datasets for selecting the appropriate approach (statistical, ML, or AI-based) - Solid grounding in classical data science methods including statistical modeling, feature engineering, and hypothesis testing - Experience in the production deployment of ML/AI systems using APIs, microservices, and cloud platforms like AWS/Azure/GCP - Familiarity with evaluation frameworks and model observability Nice to Have: - Knowledge of vector databases such as Pinecone, Weaviate, FAISS - Experience in LLM fine-tuning or RLHF - Familiarity with Databricks or other large-scale data platforms - Exposure to agentic frameworks like LangChain, LlamaIndex, AutoGen - Understanding of async/streaming architectures In addition, you should be curious and rigorous, thinking in experiments while building for production. You are expected to bridge the gap between data science and engineering, prioritizing impact, quality, and reusable foundations in your work. As a Senior AI Engineer specializing in Data Science & Generative AI, your mission is to build and productionize intelligent AI systems by combining deep data science expertise with engineering rigor to deliver scalable Gen AI solutions. Key Responsibilities: - Design and build end-to-end Gen AI and ML pipelines, from data exploration to production deployment - Develop agentic AI systems such as RAG, tool orchestration, planning/execution flows - Build retrieval services, embedding pipelines, and model-routing infrastructure - Train, fine-tune, and evaluate LLMs and ML models; implement monitoring and cost-control frameworks - Translate data science research into robust, production-grade services - Collaborate with Engineering and Product teams; contribute to AI architecture decisions Qualifications Required: - 5+ years of experience in data science, ML engineering, or AI development - Strong proficiency in Python with solid software engineering practices - Hands-on experience with Gen AI technologies such as LLMs, prompt engineering, RAG, embeddings, and agents - Deep analytical skills to explore, understand, and interpret complex datasets for selecting the appropriate approach (statistical, ML, or AI-based) - Solid grounding in classical data science methods including statistical modeling, feature engineering, and hypothesis testing - Experience in the production deployment of ML/AI systems using APIs, microservices, and cloud platforms like AWS/Azure/GCP - Familiarity with evaluation frameworks and model observability Nice to Have: - Knowledge of vector databases such as Pinecone, Weaviate, FAISS - Experience in LLM fine-tuning or RLHF - Familiarity with Databricks or other large-scale data platforms - Exposure to agentic frameworks like LangChain, LlamaIndex, AutoGen - Understanding of async/streaming architectures In addition, you should be curious and rigorous, thinking in experiments while building for production. You are expected to bridge the gap between data science and engineering, prioritizing impact, quality, and reusable foundations in your work.