[Remote] AI Data Scientist

Note: The job is a remote job and is open to candidates in USA. SoTalent is seeking an AI Data Scientist to research, develop, and deploy production-ready machine learning, NLP, and Generative AI solutions. The role combines AI research, model development and evaluation, data infrastructure, production deployment, technical leadership, and cross-functional collaboration.

Responsibilities

• Research emerging advancements in machine learning, deep learning, NLP, Generative AI, and foundation models
• Evaluate new algorithms, architectures, and methodologies to identify opportunities for business impact
• Translate research findings into practical, scalable product capabilities
• Explore innovative approaches to solving complex data and language-based challenges
• Stay current with advancements in AI, model architectures, tooling, and industry best practices
• Design, develop, and implement advanced NLP and machine learning solutions for complex business applications
• Build models for text classification, information extraction, semantic search, document intelligence, entity recognition, recommendation systems, and predictive analytics
• Develop feature engineering strategies, embeddings, training datasets, and data representations
• Apply both traditional NLP methodologies and state-of-the-art transformer-based approaches
• Continuously improve model performance through experimentation and optimization
• Develop and deploy Generative AI solutions powered by Large Language Models (LLMs) and foundation models
• Design and implement Retrieval-Augmented Generation (RAG) architectures and knowledge retrieval frameworks
• Build AI-powered workflows utilizing prompt engineering, structured generation, embeddings, vector search, reranking, and agent-based systems
• Fine-tune and optimize foundation models to improve domain-specific performance and accuracy
• Evaluate and implement emerging AI frameworks and orchestration technologies
• Develop scalable data pipelines and preprocessing workflows supporting machine learning and AI systems
• Build robust architectures for managing structured and unstructured datasets at scale
• Design processes that ensure data quality, consistency, governance, and reliability
• Collaborate with engineering teams to create production-ready model deployment and monitoring solutions
• Support cloud-native AI infrastructure and distributed data processing environments
• Design rigorous experiments, benchmarks, evaluation datasets, and testing methodologies
• Develop frameworks for measuring model quality, business impact, and operational performance
• Perform statistical analysis, cross-validation, and hyperparameter optimization
• Conduct detailed error analysis to identify improvement opportunities and performance bottlenecks
• Establish evaluation standards for NLP, machine learning, and Generative AI initiatives
• Transition AI solutions from research and prototyping into reliable production environments
• Partner with engineering teams to deploy, monitor, and optimize machine learning systems
• Implement model monitoring, drift detection, performance tracking, and automated retraining processes
• Improve scalability, reliability, and operational efficiency of AI-powered applications
• Contribute to MLOps best practices, CI/CD workflows, and AI lifecycle management
• Serve as a technical leader and subject matter expert in NLP, machine learning, and Generative AI
• Mentor data scientists, engineers, and researchers on modeling techniques, experimentation, and AI best practices
• Review technical designs, research approaches, and implementation strategies
• Foster a culture of innovation, collaboration, and evidence-based decision-making
• Promote scientific rigor and responsible AI development practices
• Partner with product managers, engineers, business stakeholders, and analytics teams to define AI strategy and priorities
• Translate complex business objectives into measurable AI and machine learning solutions
• Communicate technical concepts and research findings to both technical and non-technical audiences
• Influence product roadmaps and strategic decisions through data-driven insights and AI innovation
• Drive alignment between AI capabilities and business objectives

Skills

• Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Computational Linguistics, Engineering, or a related quantitative discipline
• 10+ years of professional experience in Data Science, Artificial Intelligence, Machine Learning, or NLP-focused roles
• Demonstrated success delivering machine learning, NLP, or Generative AI solutions from research through production deployment
• Strong ability to convert open-ended research questions into scalable business solutions
• Extensive experience collaborating within cross-functional product and engineering environments
• Expert-level proficiency in Python
• Advanced SQL skills and experience working with large-scale structured and unstructured datasets
• Strong statistical analysis, hypothesis testing, and experimental design expertise
• Experience with distributed data processing platforms such as Spark, Databricks, or BigQuery
• Deep expertise in machine learning, NLP, and deep learning methodologies
• Experience with PyTorch, TensorFlow, Hugging Face Transformers, spaCy, NLTK, and Sentence Transformers
• Knowledge of text classification, information retrieval, semantic search, document intelligence, topic modeling, named entity recognition, and knowledge extraction
• Strong understanding of neural networks, transformers, attention mechanisms, encoder-decoder architectures, CNNs, RNNs, LSTMs, and GRUs
• Hands-on experience with Large Language Models and Generative AI architectures
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings & Vector Search
• Fine-Tuning & Instruction Tuning
• LoRA / QLoRA
• AI Agents & Agentic Workflows
• Structured Generation
• LLM Evaluation Methodologies
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
• MLflow
• Kubeflow
• Weights & Biases
• Docker
• Kubernetes
• Ray
• Experience deploying scalable machine learning systems into production environments
• Pinecone
• FAISS
• Weaviate
• Milvus
• Elasticsearch/OpenSearch
• LangChain
• LlamaIndex
• DSPy
• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, Statistics, or related discipline
• Experience applying AI to document intelligence, semantic search, legal document processing, knowledge management, or enterprise search
• Experience with multimodal AI systems and foundation models
• Knowledge of knowledge graphs, synthetic data generation, inference optimization, model quantization, and distillation techniques
• Experience developing distributed LLM training or inference architectures
• Background working within Agile product development environments

Benefits

• Remote, US

Company Overview