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Tier-1 brand, metro location, mid-level years requirement, and broad tech stack increase applicant competition.
Regulated financial KYC and risk focus, plus LLM governance needs, make background fit highly industry-specific.
Multiple mandatory skills (LLM, data engineering, cloud) and an explicit 5+ years requirement enforce strict shortlisting.
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Develop and maintain secure, scalable data-intensive applications and platforms supporting global KYC and risk assessment.
Design and govern agentic AI systems and establish engineering standards for LLM-based applications ensuring safety and observability at scale.
Lead architecture definition, set engineering standards across teams, and advise on technology within the software development lifecycle including AI-assisted automation.
5+ years of applied experience with formal software engineering training or certification.
Expertise in Python and/or Java programming languages with hands-on experience in system design, application development, testing, and operational stability at enterprise scale.
Proven experience designing and deploying production AI/ML systems, including LLM-based applications in regulated environments.
Practical cloud-native experience with AWS, Azure, or GCP and advanced knowledge of data engineering technologies such as Kafka, Redis, NoSQL databases, orchestration frameworks, and observability tools.
Experienced in large-scale data processing and engineering in financial or regulated environments with strong knowledge of AI/ML and data governance.
Capable of leading cross-functional teams and driving adoption of advanced technical engineering standards across multiple teams.
Skilled at communicating complex technical concepts to senior leaders and comfortable working with modern data platforms and LLM orchestration frameworks.