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Strong employer brand, metro location, and broad ML skillset increase candidate competition and visibility.
Core ML engineering skills are transferable, but consulting and stakeholder experience raise required domain fit to medium.
Explicit senior experience band and many mandatory ML, cloud, and deployment skills create strict screening filters.
Design, develop, and deploy advanced AI and ML models including NLP, computer vision, and predictive analytics for real-world business applications.
Lead end-to-end AI solution development including data preprocessing, model deployment, monitoring, and collaborating with cross-functional teams to implement AI strategies.
Provide technical leadership and mentorship to junior team members while ensuring model performance, reliability, and ethical AI practices are followed.
7–10 years of relevant experience in AI/ML development.
Strong proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
Experience with cloud platforms (AWS, Azure, or GCP) and deploying AI models using APIs, microservices, or MLOps frameworks.
Bachelor of Engineering degree (BE/B.Tech/M.Tech) in relevant field.
Experienced in building scalable AI models with strong skills in machine learning, deep learning, and statistical modeling techniques.
Capable of leading AI projects with a focus on delivering measurable business impact and collaborating across teams.
Knowledgeable about state-of-the-art AI methods including generative AI, LLMs, MLOps tools, and ethical AI practices.