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.