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