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Niche ML/AI skillset and lesser-known employer create moderate competition and filtering.
Highly domain-specific ML/AI expertise reduces cross-industry transferability.
Multiple mandatory ML, cloud, and MLOps skills plus explicit seniority make screening stringent.
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Lead end-to-end design, development, deployment, and optimization of scalable AI/ML solutions for enterprise applications.
Architect and maintain machine learning pipelines and production-ready AI systems focusing on Generative AI, NLP, Computer Vision, and MLOps.
Mentor junior AI/ML engineers and collaborate with cross-functional teams to translate business needs into AI-driven outcomes.
6-9+ years of experience in AI, Machine Learning, or Data Science with production deployment expertise.
Mandatory proficiency in Python programming and experience with ML frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
Experience with cloud AI platforms (AWS, Azure, GCP), MLOps tools (Docker, Kubernetes, MLflow), and model deployment.
Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, or related field.
Experienced in leading end-to-end AI projects with a focus on scalable architectures and enterprise deployments.
Strong expertise in Generative AI, Large Language Models, NLP, Computer Vision, and MLOps with cloud-native AI services.
Demonstrated ability to mentor teams, drive AI initiatives, and work in Agile environments collaborating with cross-functional stakeholders.