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Senior AI Engineer / Data Scientist

Remotive
17 hours ago
Full-time
On-site
East New York, New York, United States
Role Description

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.

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.

Requirements

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.