Senior AI Engineer

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<\/span>
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●       <\/span><\/span><\/span><\/span>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.<\/span>
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●       <\/span><\/span><\/span><\/span>Exhibit excellent communication, presentation, and stakeholder management skills<\/span><\/b> to clearly articulate technical findings, proposals, and project status to both technical and non -technical audiences.<\/span>
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●       <\/span><\/span><\/span><\/span>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.<\/span>
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●       <\/span><\/span><\/span><\/span>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.<\/span>
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●       <\/span><\/span><\/span><\/span>Manage underlying solution infrastructure, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and database systems.<\/span>
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●       <\/span><\/span><\/span><\/span>Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high -performance data processing (e.g., using technologies like Apache Spark).<\/span>
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●       <\/span><\/span><\/span><\/span>Contribute to the strategic growth of the ML Practice Team, including participation in technical assignments and knowledge transfer activities.<\/span>
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●       <\/span><\/span><\/span><\/span>Ensure all client engagements and training activities are properly documented and reported via designated partner platforms.<\/span>
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Required Qualifications<\/span><\/b>
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●       <\/span><\/span><\/span><\/span>4+ years<\/span><\/b> of hands -on professional experience developing, deploying, and managing Machine Learning models, with a mandatory requirement for productionizing<\/b> and maintaining models in a live environment.<\/span>
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●       <\/span><\/span><\/span><\/span>3+ years<\/span><\/b> of experience in a customer -facing consulting or solutions architect role, focused on technical implementation and delivery.<\/span>
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●       <\/span><\/span><\/span><\/span>Excellent verbal and written communication skills<\/span><\/b> for effective client and internal team interaction.<\/span>
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●       <\/span><\/span><\/span><\/span>Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices.<\/span>
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●       <\/span><\/span><\/span><\/span>Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration.<\/span>
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●       <\/span><\/span><\/span><\/span>Deep understanding of programming for data -intensive and scalable ML applications.<\/span>
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●       <\/span><\/span><\/span><\/span>Proven experience in deploying and managing Generative AI and NLP solutions for client applications.<\/span>
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Preferred Qualifications<\/span><\/b>
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●       <\/span><\/span><\/span><\/span>Hands -on experience with modern ML platform stacks, such as Databricks MLOps Stacks.<\/span>
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●       <\/span><\/span><\/span><\/span>Knowledge of specific tools and techniques used in scalable machine learning and large -scale data processing.<\/span>
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●       <\/span><\/span><\/span><\/span>Demonstrated commitment to continuous learning in emerging ML fields, such as LLMs and GenAI application architectures.<\/span>
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