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Mid-level AI role with popular ML skills and metro office context creates moderate candidate competition.
Role requires production ML/MLOps and domain-specific AI expertise, limiting transferability across unrelated backgrounds.
Explicit 5–7 years requirement plus mandatory production ML, cloud, and deployment skills makes filtering strict.
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Design, develop, and deploy scalable AI models and end-to-end ML workflows across enterprise platforms, ensuring alignment with governance, risk, and compliance requirements.
Collaborate cross-functionally with data scientists, engineers, product managers, and business stakeholders to deliver AI-enabled solutions and drive AI capability advancement.
Mentor junior AI engineers and drive continuous improvement in AI development, automation, and operational efficiency.
5–7 years of hands-on experience in AI/ML/data science with production deployment of ML models.
Proficiency in Python 3.x and AI frameworks such as TensorFlow and PyTorch for AI/ML development and deployment.
Experience with cloud platforms (AWS, Azure, or GCP) for model training, deployment, and monitoring.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
Experienced in regulated or enterprise environments, capable of integrating AI solutions within governance and compliance frameworks.
Practiced in end-to-end AI project ownership, including data pipeline construction, model monitoring, retraining, and documentation.
Skilled at collaborating with diverse technical and business teams to translate requirements into production-ready AI workflows and solutions.