Data Scientist III

Job Description About The Role: We are seeking a Technical Data Scientist / AI Engineer with 4-5 years of commercial experience blending traditional data science with cutting-edge artificial intelligence. You are an autonomous developer who writes production-grade Python, possesses deep foundations in Machine Learning (ML) and Deep Learning (DL), and has hands-on experience building generative workflows like Retrieval-Augmented Generation (RAG) and autonomous AI Agents. You will be responsible for bringing models out of notebooks and into robust, scalable software environments. Responsibilities ML/DL System Development: Design, train, and optimize custom machine learning algorithms and deep neural networks (Transformers, CNNs, or RNNs) to solve complex business predictive problems. GenAI Agent Design: Build and orchestrate production-level autonomous AI Agents and Multi-Agent structures using Python frameworks (e.g., LangGraph, CrewAI, AutoGen). Advanced RAG Pipelines: Architect high-throughput RAG systems, managing the complete pipeline across semantic chunking, multi-stage retrieval, embedding generation, reranking, and vector store integration. Python Engineering: Write clean, modular, and performance-optimized Python code. Enforce high standard-engineering practices including unit testing, Git version control, and CI/CD alignment. Cloud Infrastructure Alignment: Assist in porting localized AI and ML workflows into cloud environments, ensuring proper resource allocation for heavy training and inference workloads. Requirements Experience: 3 to 4 years of professional, hands-on experience working as a Data Scientist, ML Engineer, or AI Developer in a production software environment. Core Toolkit: Proven track record building applications using Python and foundational libraries such as scikit-learn, PyTorch, or TensorFlow. Generative AI Stack: Direct experience manipulating LLMs, implementing vector databases (e.g., Pinecone, Qdrant, Milvus, Chroma), and tuning systemic prompt graphs. Data Engineering Foundations: Proficient in writing optimized SQL, data cleaning, and structuring pipelines utilizing pandas or NumPy. AWS Knowledge (Good to Have): Experience building, testing, or serving models using cloud-native platforms like Amazon SageMaker AI or orchestrating managed endpoints via Amazon Bedrock. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.