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Mid-level metro AI role with broad requirements but niche agentic specialization moderates applicant density.
Specialized agentic AI and LLMOps requirements increase domain specificity and reduce cross-industry transferability.
Explicit 6+ years, 3+ years AI experience, and mandatory LLMOps/agentic AI skills create strict filters.
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Own development and delivery of the Agentic AI Automation Platform that automates complex banking workflows.
Design and implement modular, context-aware AI agent systems leveraging modern agent patterns (MCP, A2A, ACP).
Manage AI application lifecycle including model fine-tuning, prompt versioning, monitoring, and experimentation using tools like Azure AI Foundry and HuggingFace.
Minimum 6 years of software engineering experience, with 3+ years specifically in enterprise-grade AI application development.
Proficiency in Python with deep experience in Agentic AI frameworks, architectures, knowledge-base, AI search, retrieval-augmented generation, context engineering, evaluation, and guardrailing.
Hands-on experience with MLOps/LLMOps practices including model lifecycle management, prompt versioning, fine tuning, and agent monitoring.
Not explicitly mentioned in the JD: mandatory degree requirements or specific location constraints.
Experienced in building modular, collaborative AI agent systems for complex and dynamic workflows in enterprise settings.
Comfortable working in a fast-paced startup-like environment requiring autonomy and speed of execution.
Strong communication and documentation skills with a bias towards knowledge sharing and scalable engineering solutions.