Senior AI Engineer at Koantek

Chesterfield, Missouri, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Feb, 26

Salary

0.0

Posted On

08 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, MLOps, Generative AI, Natural Language Processing, Client-Facing Implementation, Production-Grade Solutions, Docker, Apache Spark, Data Pipeline Orchestration, Continuous Integration, Continuous Delivery, Stakeholder Management, Technical Consulting, Infrastructure Management, Model Versioning, High-Performance Data Processing

Industry

IT Services and IT Consulting

Description

Sr AI Engineer / Data Scientist / MLOps Consultant Location: 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.

Responsibilities

The successful candidate will lead and execute end-to-end ML project implementations directly with clients, translating complex business problems into robust technical solutions. They will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models.