Data Scientist / MLOps

US Citizen or Green Card required Strong Contract to Hire No C2C Requests please Hiring an experienced and highly technical Data Scientist to join a customer-facing consulting team. Requires a blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. The successful candidate will be instrumental in designing, deploying, and maintaining production-grade ML solutions, including advanced Generative AI and Natural Language Processing (NLP) models, for a diverse client base. Responsibilities • Serve as a primary technical consultant, leading and executing end-to-end ML project implementations directly with clients, translating complex business problems into robust technical solutions. • Exhibit excellent communication, presentation, and stakeholder management skills to clearly articulate technical findings, proposals, and project status to both technical and non-technical audiences. • Design, build, and maintain production-grade ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps practices to ensure reliability and scalability of models. • Implement and optimize cutting-edge Generative AI and NLP applications, demonstrating hands-on experience with technologies like Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) in a production setting. • Manage underlying solution infrastructure, demonstrating proficiency in technologies such as Docker, pipeline orchestraters, and database systems. • Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark). Required Qualifications • 4+ years of hands-on professional experience developing, deploying, and managing Machine Learning models, with a mandatory requirement for productionizing and maintaining models in a live environment. • 3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical implementation and delivery. • Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices. • Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration. • Deep understanding of programming for data-intensive and scalable ML applications. • Proven experience in deploying and managing Generative AI and NLP solutions for client applications. • Hands-on experience with modern ML platform stacks, such as Databricks MLOps Stacks. • Knowledge of specific tools and techniques used in scalable machine learning and large-scale data processing. • Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI application architectures.