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Metro location, mid-level (5 years), broad ML skillset, and popular ML title increase applicant competition.
Advanced ML, MLOps, and cloud skills are transferable across industries, giving moderate sensitivity.
Explicit 5+ years plus mandatory ML/DL frameworks, cloud, MLOps, and data engineering makes filters stringent.
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Architect, design, and deploy scalable AI/ML solutions on cloud platforms (AWS, GCP, Azure).
Lead development and optimization of complex AI models (NLP, computer vision, generative AI, predictive analytics) and oversee large-scale data engineering pipelines.
Implement and maintain MLOps/LLMOps processes, mentor junior engineers, and collaborate cross-functionally to deliver AI solutions aligned with business needs.
5+ years of professional experience in AI/ML or related technical domain.
Proficient in Python and ML/DL frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.
Experience with cloud-based ML deployment platforms (AWS SageMaker, VertexAI, etc.).
Strong expertise in advanced AI techniques (NLP including LLMs/transformers, computer vision, generative AI), MLOps/DevOps (Docker, Kubernetes, CI/CD, MLflow, DVC), and data engineering (ETL, batch/streaming data, SQL/NoSQL).
Experienced senior engineer comfortable owning end-to-end AI/ML solution lifecycles including architecture, deployment, and optimization.
Demonstrated leadership in mentoring teams and managing delivery of large-scale AI projects in enterprise or product settings.
Technical proficiency with cutting-edge generative AI frameworks and cloud-native architectures and a background aligned with enterprise automation or digital transformation initiatives.