Job Description

Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base. Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings. * Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory). * 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration. * Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks. * Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.