Job DescriptionLocation: United States - Remote
Employment Type: Full-Time and Contract
We are seeking an experienced and highly technical Data Scientist to join our customer-facing consulting team. This remote role 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 our diverse client base.Key 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 orchestrators, 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).
• Contribute to the strategic growth of the ML Practice Team, including participation in technical assignments and knowledge transfer activities.
• Ensure all client engagements and training activities are properly documented and reported via designated partner platforms.
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.
• Excellent verbal and written communication skills for effective client and internal team interaction.
• 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.
Preferred Qualifications
• 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.
Requirements• 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.
RequirementsRequired 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.
• Excellent verbal and written communication skills for effective client and internal team interaction.
• 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.
Preferred Qualifications
• 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.
Requirements
• 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.
Benefits- Work on frontier AI and data projects with Fortune 500 companies
- Contribute to IP, reusable accelerators, and real business impact
- Be part of a high-performance, engineering-first culture
