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Tier-1 brand plus mid-level ML role and generative-AI visibility drive high competition.
ML, MLOps and Python are transferable, but financial/regulatory domain knowledge raises specificity.
Requires production ML/LLM experience, MLOps and specific technical stack, raising hiring strictness.
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Design, build, and deploy machine learning models and AI solutions to improve operational efficiency, service resilience, and customer outcomes in an enterprise-scale environment.
Develop predictive analytics and generative AI applications, evaluate emerging AI technologies, and transition AI products from concept to production with defined success metrics.
Collaborate cross-functionally with engineering, product, and business teams while supporting model governance, monitoring, and continuous improvement activities.
Demonstrable experience delivering machine learning or AI solutions in large-scale enterprise environments.
Strong proficiency in Python data science ecosystem (Pandas, NumPy, Scikit-learn), SQL, and cloud-based data science platforms with MLOps practices.
Experience with machine learning techniques including classification, regression, clustering, anomaly detection, as well as Generative AI, LLMs, RAG architectures, or agent-based AI solutions.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in translating complex business problems into measurable analytical outcomes and deploying models into production environments.
Skilled in working across multiple teams (engineering, product, operations) in a regulated or financial services setting would be advantageous.
Capable of defining success metrics and measurement frameworks, promoting responsible AI practices, and supporting AI product roadmaps and prioritization.