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Remote mid-level ML role with popular title and 5+ years experience increases applicant competition.
Strong MDM, entity-resolution and LLM requirements reduce cross-industry transferability.
Explicit 5+ years and domain-specific ML/MDM/LLM expertise make shortlisting highly selective.
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Design and optimize LLM-driven entity resolution systems to improve match accuracy, explainability, and performance within enterprise data environments.
Develop and refine prompt engineering strategies, Retrieval-Augmented Generation (RAG) architectures, and evaluation frameworks to enhance data matching and trust.
Collaborate with cross-functional teams to translate business requirements into scalable AI/ML solutions and build user-facing tools for transparency and control over matching decisions.
5+ years of hands-on experience building and deploying machine learning solutions in production, preferably as ML Engineer or Data Scientist.
Deep expertise in Entity Resolution and Master Data Management (MDM), including data matching, deduplication, and survivorship.
Strong practical experience with Generative AI technologies including Large Language Models (LLMs), Vector Search, and Retrieval-Augmented Generation (RAG) architectures.
Proficiency in Python with ML frameworks like PyTorch, TensorFlow, or scikit-learn, plus production deployment and optimization of AI/ML models focusing on latency, throughput, and cost.
Experienced in enterprise-scale entity matching and MDM systems with a strong focus on precision, recall, and operational efficiency in production.
Skilled in designing, iterating, and deploying advanced LLM-based AI architectures such as RAG, balancing performance and cost in production environments.
Demonstrated ability to build production-grade AI tools that interface directly with business users and work closely with cross-functional teams to implement AI solutions aligned with complex business requirements.