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Tier-1 brand and Bengaluru location boost applicant density despite niche AI orchestration specialization.
Core ML and data skills are transferable, but financial services cybersecurity and governance needs raise domain specificity.
Explicit seniority, years in AI and data engineering, and regulated-finance governance skills enforce strict filtering.
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Design and operationalize scalable AI orchestration frameworks integrating multiple AI models, agents, APIs, and enterprise workflows to improve cybersecurity functions.
Develop and implement Retrieval-Augmented Generation (RAG) systems, agentic workflows, and reusable AI platform components for business automation and analytics.
Establish MLOps/LLMOps deployment, monitoring, governance, Responsible AI guardrails, and AI observability within a highly regulated financial services environment.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Artificial Intelligence, or equivalent practical experience.
10-15 years of experience in Data Engineering, Machine Learning Engineering, Software Engineering, or related disciplines.
3-5 years of experience specifically in AI Engineering including Prompt Engineering, Gen AI, Agentic AI, MCP Framework, or RAG Architecture.
Strong experience with Python, SQL, API development, workflow automation, cloud-native architectures, and large-scale data pipelines.
Experienced in building enterprise AI capabilities within regulated financial services or similarly complex environments.
Proficient in designing agentic AI workflows and orchestration frameworks that enhance operational efficiency and cybersecurity resilience.
Demonstrated ability to integrate AI governance, observability, model risk management, and responsible AI principles into production systems.