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Tier-1 brand and remote flexibility increase competition, but senior ML specialization reduces applicant pool.
Advanced ML/AI and LLM expertise is transferable across industries but demands specialized experience.
Extensive ML/AI, LLM, and MLOps technical requirements imply high shortlisting strictness.
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Develop and deploy predictive and prescriptive models using statistical and machine learning algorithms including time series, regression, decision trees, ensembles, and neural networks (CNN, LSTM, Transformers).
Handle data engineering tasks across multiple programming languages (Python, R), databases (RDBMS, NoSQL), and cloud platforms (Azure, AWS, GCP).
Implement advanced analytics such as NLP (including LLMs and agentic AI applications), graph analytics, mathematical optimization, simulations, and ML model deployment with ML-Ops on-premises and cloud environments.
Strong experience with Python (NumPy, SciPy, Pandas, MatPlotLib, Seaborne) and ML/DL frameworks (Scikit-Learn, TensorFlow, PyTorch).
Experience in predictive modelling including time series, regression, ensembles, neural networks, and NLP tasks.
Familiarity with databases including MySQL, Oracle, HBase, Cassandra and cloud platforms Azure, AWS, GCP.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and optimizing knowledge graphs and advanced graph algorithms (BFS, DFS, Dijkstra, PageRank, TrustRank).
Comfortable working with mathematical optimization techniques (linear, mixed-integer, dynamic) and simulation methods (Monte Carlo, agent-based, system dynamics).
Able to manage end-to-end ML pipelines with strong focus on data security, production readiness, and ML-Ops for cloud and on-premise deployments.