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Tier-1 brand, metro location, and mid-level LLM role attracts many qualified applicants.
Core LLM and engineering skills transfer broadly, but regulated banking experience increases fit sensitivity.
Requires specific LLM, Python, cloud, security, and production experience, enforcing strict candidate filters.
Design, build, and productionize LLM-powered applications including conversational interfaces, intelligent search, and advisory assistants.
Implement secure tool integrations and build RAG components to ensure data quality, latency, and audit compliance.
Develop architecture/design artifacts and establish evaluation/monitoring mechanisms for distributed AI systems with focus on performance and responsible AI usage.
3+ years of applied software engineering experience with formal training or certification.
Strong proficiency in Python and experience with APIs/microservices.
Hands-on experience with LLM, RAG, or agentic applications and familiarity with agent frameworks like LangChain, LangGraph, or similar.
Experience with cloud platforms (AWS or Azure), CI/CD, observability, secure development practices, and using enterprise-authorized AI-assisted software development tools.
Experienced in building production-scale AI systems involving multi-step reasoning and tool orchestration with attention to security and audit requirements.
Proficient in applying responsible AI principles and guiding peers on safe AI usage in engineering workflows under regulated or high-security environments.
Skilled in data handling, monitoring, and tuning AI features within large distributed systems, ensuring system reliability and compliance.