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Tier-1 brand and metro location increase competition; seniority and niche agentic-AI reduce applicant density.
Core ML/LLM engineering skills transfer across industries, but banking/regulatory and agentic-AI experience increase domain specificity.
Explicit 10+ years, 3+ years AI, mandatory ML/LLM stack, Docker/Kubernetes and banking familiarity heighten filtering.
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Lead design, hands-on development, and deployment of scalable agentic AI frameworks and generative AI solutions tailored for banking use cases.
Architect and integrate full-stack AI applications using advanced ML/LLM tools ensuring high performance, reliability, and security.
Drive continuous AI system optimization via rigorous metrics, rapid MVP iterations, and cross-functional technical leadership.
10+ years in software engineering with strong hands-on coding and rapid delivery of AI features to production.
Minimum 3+ years focused on AI software development including prompt engineering, machine learning, generative or agentic AI systems.
Proficient in Python (FastAPI, Django, Flask, PySpark) and experienced with ML frameworks (TensorFlow, PyTorch) and libraries (Scikit-Learn, NumPy, Pandas).
Bachelor’s degree in Computer Science, IT, AI, Robotics, or related quantitative field; Master’s preferred.
Deep expertise with agent-based AI systems and model integration protocols within enterprise banking technology.
Experience leading architecture decisions involving microservices, API-first design, Docker/Kubernetes, and secure, resilient AI operations.
Prior exposure to banking or financial services regulatory environments and large-scale AI project delivery.