Back
Senior metro ML role with broad GenAI and production requirements yields medium competition density.
Role requires deep ML/DL/NLP and production AI expertise, making cross‑industry transferability low (high sensitivity).
Explicit 8–15 years plus mandatory hands‑on ML, DL, GenAI, and production experience enforces high strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end design, development, deployment, and optimization of machine learning and AI solutions for enterprise-scale business problems across commercial operations, supply chain, and customer experience.
Build supervised and unsupervised ML models, including deep learning and NLP models using TensorFlow and PyTorch, ensuring production readiness and scalability.
Collaborate with data engineering and cross-functional teams to manage enterprise data, develop scalable data pipelines, and integrate AI solutions on cloud platforms (AWS/GCP/Azure).
8–15 years of strong hands-on Data Science and Machine Learning experience with proven end-to-end solution ownership in enterprise environments.
Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering or related field; Master’s (M.Tech/PGP/PGD) in Data Science or Business Analytics preferred.
Proficiency in Python and libraries/frameworks: Pandas, NumPy, scikit-learn, TensorFlow and/or PyTorch; experience in supervised/unsupervised learning, regression, classification, clustering, time series, deep learning, NLP.
Work location: Hyderabad, India with Hybrid work mode.
Senior individual contributor comfortable handling complex technical problems and delivering scalable machine learning solutions in enterprise settings.
Deep expertise in classical Data Science and ML combined with practical experience in Generative AI tools like LangChain, HuggingFace, embeddings, and vector databases.
Experience or exposure to industrial/manufacturing or enterprise commercial domains and working with large-scale enterprise data platforms and cloud deployments.