Senior AI Engineer

Koantek; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Koantek is seeking a Sr AI Engineer/Data Scientist/ML Ops Consultant for a remote, full-time/contract role in the United States. The candidate will lead client-facing ML projects, deploy production-grade models, and advance MLOps practices across diverse client engagements.

Responsibilities include designing end-to-end ML solutions, implementing Generative AI/NLP applications (RAG/LLMs), and managing scalable infrastructure with Docker, Spark, and orchestration tools.

Qualifications

• 4+ years of hands-on experience deploying ML models in production.
• 3+ years in a customer-facing consulting or solutions-architect role.
• Proven ability to design and maintain production ML pipelines and MLOps processes.

Responsibilities

• Serve as a technical consultant, leading end-to-end ML projects with clients.
• Communicate findings and status to both technical and non-technical audiences.
• Design, build, and maintain production-grade ML pipelines with CI/CD and MLOps.
• Implement Generative AI and NLP applications using RAG and LLMs in production.
• Manage infrastructure using Docker, orchestrators, and databases.
• Leverage distributed computing with Spark for scalable ML workloads.
• Contribute to growth of the ML Practice Team and knowledge transfer.
• Document client engagements and training activities on partner platforms.

Skills

MLOps lifecycle management

Generative AI

NLP

Distributed computing

Client communication

Tools

Docker

Databricks MLOps Stacks

Pipeline orchestration

Apache Spark

Job description

Sr AI Engineer / Data Scientist / MLOps Consultant Location: United States – Remote Employment Type: Full-Time and Contract

Weare seeking an experienced and highly technical Data Scientist to join ourcustomer-facing consulting team. This remote role requires a blend of advancedMachine Learning (ML) expertise, deep knowledge of MLOps principles, and aproven track record in client-facing implementation. The successful candidatewill be instrumental in designing, deploying, and maintaining production-gradeML solutions, including advanced Generative AI and Natural Language Processing(NLP) models, for our diverse client base.

Key Responsibilities

• Serve as a primary technicalconsultant, leading and executing end-to-end ML project implementationsdirectly with clients, translating complex business problems into robusttechnical solutions.
• Exhibit excellent communication, presentation, andstakeholder management skills to clearly articulate technical findings, proposals, andproject status to both technical and non-technical audiences.
• Design, build, and maintainproduction-grade ML pipelines, focusing on continuous integration, continuousdelivery (CI/CD), and advanced MLOps practices to ensure reliability andscalability of models.
• Implement and optimizecutting-edge Generative AI and NLP applications, demonstrating hands‑onexperience with technologies like Retrieval Augmented Generation (RAG) andLarge Language Models (LLMs) in a production setting.
• Manage underlying solutioninfrastructure, demonstrating proficiency in technologies such as Docker,pipeline orchestrators, and database systems.
• Leverage expertise indistributed computing frameworks, specifically in scalable machine learning andhigh-performance data processing (e.g., using technologies like Apache Spark).
• Contribute to the strategicgrowth of the ML Practice Team, including participation in technicalassignments and knowledge transfer activities.
• Ensure all client engagementsand training activities are properly documented and reported via designatedpartner 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 modelsin 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 andinternal team interaction.
• Expertise in MLOps lifecyclemanagement, including model versioning, testing, monitoring, and automateddeployment best practices.
• Demonstrable experience withinfrastructure management, encompassing containerization (Docker) and datapipeline orchestration.
• Deep understanding ofprogramming for data-intensive and scalable ML applications.
• Proven experience indeploying 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 toolsand techniques used in scalable machine learning and large-scale dataprocessing.
• Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI applicationarchitectures.

Requirements

• Hands‑on experience with modern ML platform stacks, such as Databricks MLOps Stacks.
• Knowledge of specific toolsand techniques used in scalable machine learning and large-scale dataprocessing.
• Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI applicationarchitectures.