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Tier-1 brand, mid-level experience band, and metro location drive high competition despite niche GenAI skills.
Core GenAI and agentic engineering skills transfer across industries but banking compliance increases domain specificity.
Explicit 4-7 years and mandatory GenAI, LangChain, MLOps, Kubernetes, and compliance requirements create strict filtering.
Lead the design and development of production-grade Generative AI applications and complex Agentic solutions for Retail Banking business.
Drive analytics and AI initiatives to enhance productivity and deliver customized solutions leveraging Generative AI capabilities.
Ensure solutions comply with security, compliance, and performance standards while advising business stakeholders on Generative AI scope and capabilities.
4-7 years of experience with preferred expertise in architecting and developing production-ready Generative AI RAG or Agent-based solutions.
Mastery of Python, AI/ML frameworks such as PyTorch, TensorFlow, LangChain, MLOps, and cloud-native architectures like Kubernetes.
Bachelor's degree required; Master's degree preferred in Computer Science, AI, Engineering, or a related quantitative field.
In-depth knowledge of compliance, security, and performance management in a production environment.
Experienced in Agentic solution design, including architecture selection, memory management, hallucination control, and reinforcement learning feedback loops.
Proven ability to architect highly scalable, available, and performant technology solutions within Retail Banking or similar sectors.
Skilled in mentoring and training teams on advanced Agentic solution development and software engineering best practices.