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Strong Tier-1 brand, metro location, popular title, and mid-level role drive high competition.
Machine learning skills are transferable, but finance and regulated-experience preferences raise domain specificity to medium.
Multiple mandatory ML, MLOps, and enterprise production requirements but no explicit years, so medium strictness.
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Design, build, and deploy machine learning models to solve operational and business challenges, focusing on incident prevention, service health, and customer experience.
Develop and support AI solutions including Generative AI applications utilizing LLMs and agentic workflows, taking products from concept to production.
Evaluate AI technologies and define success metrics, contributing to product roadmaps and ensuring model monitoring and governance.
Experience delivering machine learning or AI solutions in large-scale enterprise environments.
Technical skills in Python (Pandas, NumPy, Scikit-learn), SQL, machine learning techniques (classification, regression, clustering, anomaly detection), and cloud-based data science platforms with MLOps.
Experience working with both structured and unstructured large-scale data sets.
Work Experience Required: Not explicitly mentioned in the JD.
Strong ability to translate business problems into measurable analytical outcomes and operationalize models into production environments.
Experience with Generative AI, LLMs, retrieval augmented generation (RAG) architectures, and agent-based AI solutions.
Familiarity with financial services or regulated environments is a plus, indicating fit for complex, compliance-driven domains.