AI Engineer

Responsibilities: Design, develop, and deploy enterprise AI/GenAI applications for banking use cases Architect LLM-enabled solutions including retrieval-augmented generation, vector search, and agentic workflows Build production-grade services and APIs using Python, FastAPI/Flask, Azure OpenAI, Azure ML, Databricks, ADLS Integrate AI into enterprise apps, workflows, knowledge platforms, automation, and decision-support Establish CI/CD, testing, observability, performance, security, and responsible AI controls Create reusable AI frameworks, reference architectures, and governance for enterprise adoption Collaborate with business, data, cybersecurity, risk, compliance, legal, and vendors to meet regulatory and risk requirements Prototype rapidly with stakeholders, scale pilots, and define adoption metrics Evaluate LLM platforms for accuracy, latency, cost, and security Support hiring, mentoring, and day-to-day technical leadership of AI engineers Stay current with AI tech and advise on opportunities and risks Perform other duties as assigned

Key requirements: Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience 10+ years in software/AI engineering or related leadership roles with production AI delivery experience Strong hands-on background in Python, API design, microservices, cloud architecture, distributed systems, data pipelines, CI/CD, testing, observability, and secure software delivery Deep experience with Azure ecosystem (Azure OpenAI, Azure ML, Databricks, ADLS) and enterprise integration patterns Experience with LLM tooling (LangChain, LlamaIndex, Semantic Kernel), vector databases, model registries, evaluation frameworks, and monitoring tools Strong data discipline (quality, lineage, metadata, governance) and measurable business outcomes Experience in regulated industries (financial services, banking, fintech, etc.) with compliance and governance understanding Ability to lead cross-functional teams, influence senior stakeholders, mentor engineers, and translate AI capabilities into business solutions Executive-level communication skills to define AI roadmaps, operating models, and adoption plans Cross-functional collaboration Mentoring and people leadership Strategic thinking and influencing executives Python API design Microservices