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Strong corporate brand, mid-level generalist ML role, and metro location drive high competition.
Highly specialized ML/DL and generative AI requirements limit cross-domain transferability.
Explicit years plus mandatory ML/DL, LLM, and vector-database skills create strict shortlisting filters.
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Design, train, and optimize custom machine learning and deep learning models including Transformers, CNNs, and RNNs for complex predictive business problems.
Develop and deploy production-level generative AI workflows such as Retrieval-Augmented Generation (RAG) pipelines and autonomous AI Agents using Python frameworks.
Write production-grade Python code following high standards including unit testing, version control, CI/CD, and assist in cloud migration of AI/ML systems for scalable inference and training.
3 to 4 years of professional experience as a Data Scientist, ML Engineer, or AI Developer in production software environments.
Strong proficiency with Python and libraries such as scikit-learn, PyTorch, or TensorFlow.
Hands-on experience with generative AI tools including large language model manipulation, vector databases (e.g., Pinecone, Qdrant, Milvus, Chroma), and prompt engineering.
Proficient in data engineering basics including SQL, data cleaning, and pipeline creation using pandas or NumPy.
Experienced in advanced ML engineering with focus on generative AI architectures and autonomous AI agents.
Skilled in operationalizing ML models into scalable production environments and working with cloud infrastructure like AWS SageMaker or similar.
Capable of end-to-end ownership from designing ML/DL models to production deployment emphasizing clean, modular, and performance-optimized code.