Sr AI Engineer / Data Scientist
Posted 2026-05-06Location: United States – Remote We are seeking an experienced and ● Serve as a primary technical ● Exhibit ● Design, build, and maintain ● Implement and optimize cutting-edge ● Manage underlying solution ● Leverage expertise in distributed ● Contribute to the strategic growth of ● Ensure all client engagements and Required ● 4+ ● 3+ ● Excellent ● Expertise in MLOps lifecycle ● Demonstrable experience with ● Deep understanding of programming for ● Proven experience in deploying and Preferred ● Hands-on experience with modern ML ● Knowledge of specific tools and ● Demonstrated commitment to continuous Requirements ● Hands-on ● Knowledge Required ● 4+ ● 3+ ● Excellent ● Expertise in MLOps lifecycle ● Demonstrable experience with ● Deep understanding of programming for ● Proven experience in deploying and Preferred ● Hands-on experience with modern ML ● Knowledge of specific tools and ● Demonstrated commitment to continuous ● Demonstrated commitment to continuous learning in 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
Employment Type: Full-Time and Contract
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
consultant, leading and executing end-to-end ML project implementations
directly with clients, translating complex business problems into robust
technical solutions.
excellent communication, presentation, and stakeholder management skills to clearly articulate technical
findings, proposals, and project status to both technical and non-technical
audiences.
production-grade ML pipelines, focusing on continuous integration, continuous
delivery (CI/CD), and advanced MLOps practices to ensure reliability and
scalability of models.
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.
infrastructure, demonstrating proficiency in technologies such as Docker,
pipeline orchestrators, and database systems.
computing frameworks, specifically in scalable machine learning and
high-performance data processing (e.g., using technologies like Apache Spark).
the ML Practice Team, including participation in technical assignments and
knowledge transfer activities.
training activities are properly documented and reported via designated partner
platforms.
Qualifications
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.
years of experience in a
customer-facing consulting or solutions architect role, focused on technical
implementation and delivery.
verbal and written communication skills for effective client and internal team interaction.
management, including model versioning, testing, monitoring, and automated
deployment best practices.
infrastructure management, encompassing containerization (Docker) and data
pipeline orchestration.
data-intensive and scalable ML applications.
managing Generative AI and NLP solutions for client applications.
Qualifications
platform stacks, such as Databricks MLOps Stacks.
techniques used in scalable machine learning and large-scale data processing.
learning in emerging ML fields, such as LLMs and GenAI application
architectures.
experience with modern ML platform stacks, such as Databricks MLOps Stacks.
of specific tools and techniques used in scalable machine learning and
large-scale data processing.
emerging ML fields, such as LLMs and GenAI application architectures.Requirements
Qualifications
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.
years of experience in a
customer-facing consulting or solutions architect role, focused on technical
implementation and delivery.
verbal and written communication skills for effective client and internal team interaction.
management, including model versioning, testing, monitoring, and automated
deployment best practices.
infrastructure management, encompassing containerization (Docker) and data
pipeline orchestration.
data-intensive and scalable ML applications.
managing Generative AI and NLP solutions for client applications.
Qualifications
platform stacks, such as Databricks MLOps Stacks.
techniques used in scalable machine learning and large-scale data processing.
learning in emerging ML fields, such as LLMs and GenAI application
architectures.
● Hands-on
experience with modern ML platform stacks, such as Databricks MLOps Stacks.
of specific tools and techniques used in scalable machine learning and
large-scale data processing.
emerging ML fields, such as LLMs and GenAI application architectures.Benefits