Senior AI Engineer

Koantek

  • Chesterfield, Missouri
  • 30+ days ago

    Highlights

    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. ● 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.

    Numbers & Facts

    LocationChesterfield, Missouri
    Websitewww.koantek.com

    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.



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