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Mid-level, popular Data Scientist title in a metro location with moderate brand recognition and broad applicant appeal.
Core ML/GenAI skills are highly transferable across industries; legal domain experience is only preferred.
Explicit 6–8 years plus extensive mandatory ML/GenAI, deployment, and tooling requirements imply high shortlisting strictness.
Design, develop, and deploy machine learning and Generative AI (GenAI) solutions including Retrieval-Augmented Generation (RAG) pipelines and Large Language Models (LLMs) fine-tuning in production environments.
Implement and optimize scalable ML pipelines in Python working with structured and unstructured data, collaborating with engineering, product, and SMEs to deliver AI-driven features.
Monitor model performance, conduct A/B testing and benchmarking, and contribute to AI system architecture design with focus on model evaluation, drift, and reliability.
6–8 years of professional experience in data science and machine learning roles.
Advanced proficiency in Python and experience with supervised/unsupervised learning, NLP, Generative AI (LLMs, prompt engineering, embeddings), and RAG architecture.
Hands-on with ML & AI tools such as LangChain/LlamaIndex, Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, HuggingFace, OpenAI APIs, and vector databases like Pinecone, FAISS, Weaviate, or OpenSearch.
Experience with cloud platforms (AWS/Azure/GCP), CI/CD for ML deployments, and model tracking tools (preferably MLflow).
Experienced in implementing advanced GenAI and RAG solutions, including prompt engineering and large language model fine-tuning.
Comfortable working across engineering, product, and subject matter expert teams to operationalize AI features in production.
Familiar with ML model monitoring, evaluation frameworks, experimentation, and production-scale ML infrastructure.