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Hot GenAI skillset and metro locations increase applicant density despite mid-tier employer.
Specialized LLM, multimodal, and MLOps expertise makes candidates from AI-focused backgrounds far more suitable.
Many mandatory technical and domain filters: LLMs, PyTorch/TensorFlow, MLOps, cloud, and regulated-compliance experience.
Lead design, training, and deployment of large language models and multimodal AI agents to support enterprise automation and insights.
Develop and automate scalable AI pipelines for real-time inference, retraining, and model monitoring in cloud environments.
Collaborate with cross-functional teams to translate business use cases into operational AI systems ensuring performance, fairness, and compliance.
4+ years experience in enterprise AI/ML projects involving large language models, retrieval-augmented generation, and multimodal systems.
Proficient in Python (3.8+), PyTorch, TensorFlow, and experience with AI frameworks supporting LLM training and deployment.
Experience deploying AI models on cloud platforms (AWS, Azure, or GCP) with scalable model lifecycle management (MLflow, Kubeflow).
Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related field. Hybrid work model requiring 3 days a week onsite at client office.
Experienced in operating at the intersection of AI model development and enterprise deployment with emphasis on responsible AI (bias, fairness, security).
Able to lead AI engineering efforts coordinating with data scientists, platform engineers, and business stakeholders to deliver scalable and compliant AI systems.
Strategic thinker focused on scalable, secure, and ethical AI solutions supporting regulated, complex enterprise environments.