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Specialized GenAI skills but metro location and mid-level seniority create moderate applicant density.
Role requires deep LLM/MLOps expertise and enterprise compliance experience, limiting cross-industry transferability.
Explicit years, specific LLM/MLOps stack, and enterprise/regulatory experience make screening stringent.
Lead design, training, and deployment of large language models (LLMs) and multimodal AI agents for enterprise automation.
Develop and automate scalable AI pipelines for training, inference, retraining, and model monitoring in cloud environments.
Collaborate with cross-functional teams to implement secure, compliant, and responsible AI solutions supporting business processes.
4+ years of experience in enterprise AI/ML projects including LLMs, retrieval-augmented generation, and multimodal systems.
Proficient in Python (3.8+), PyTorch, TensorFlow, and relevant AI frameworks supporting large-scale model training and deployment.
Experience with cloud platforms (AWS, Azure, or GCP) for scalable AI model deployment and orchestration.
Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical field.
Experienced with full AI/ML model lifecycle management including fine-tuning, deployment, monitoring, and bias/fairness evaluation in regulated enterprise settings.
Skilled in deploying and automating MLOps pipelines, versioning, and security compliance for scalable AI systems in hybrid cloud environments.
Demonstrated ability to collaborate with data scientists, engineers, and business stakeholders to translate use cases into operational AI workflows with measurable impact.