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Mid-level 3-6 year applied AI role with broad GenAI and cloud skill requirements increases competition density.
Requires strong ML/NLP/GenAI expertise, moderately transferable across industries.
Multiple mandatory ML, GenAI, cloud, deployment, and governance requirements create strict shortlisting filters.
Lead end-to-end development and operationalization of machine learning and deep learning models, including experimentation, evaluation, deployment, and monitoring in production environments.
Apply advanced NLP and Generative AI techniques, including embeddings, retrieval strategies, prompt engineering, and RAG solutions, to solve real-world business problems.
Ensure AI model compliance with enterprise governance, privacy, ethical standards, and translate analytical outcomes into measurable business impact.
3+ years of experience applying deep learning architectures in practical use cases.
Proficient in Python with extensive experience in pandas, NumPy, scikit-learn, strong SQL skills; familiarity with PyTorch and/or TensorFlow preferred.
Experience working with cloud-based AI platforms such as Google Vertex AI, AWS SageMaker, or Azure AI Services for model training and deployment.
Experience with NLP, Generative AI techniques, and deploying models within complex enterprise environments.
Experienced in full ML lifecycle including problem framing, experiment design, model evaluation/monitoring, and production validation.
Skilled at operating in enterprise contexts with strong governance, compliance, and ethical standards adherence.
Capable of communicating complex technical decisions and AI risks effectively to both technical and non-technical stakeholders.