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Mid-level ML/GenAI role with broad required skillset attracts many applicants.
Core ML/AI skills are transferable, but telecom domain preference increases domain specificity to medium.
Explicit 3–5 years plus mandatory GenAI, MLOps, and specific toolstack enforces strict filters.
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Build, deploy, and maintain end-to-end Machine Learning solutions at scale, emphasizing Generative AI models and frameworks.
Manage full ML model lifecycle including data preprocessing, model training, and production deployment (MLOps).
Leverage cloud platforms and ML deployment tools to operationalize AI/ML systems, preferably within the Telecom domain.
3 to 5 years of relevant experience in AI/ML roles.
Strong hands-on expertise with Generative AI models (GPT, BERT, Llama, LangChain, RAG pipelines).
Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers.
Must be willing to work full-time from office (5 days a week).
Experience in Telecom domain datasets or use cases is a strong advantage.
Comfortable managing end-to-end ML workflows including MLOps and cloud deployments.
Practical knowledge of big data tools (Spark, Hive) and SQL for data engineering pipelines.