Applied AI ML Lead - Global Banking

Job Responsibilities 

• Build and productionize agentic AI solutions agents, orchestrators, tool/function integrations, workflow/state management, and guardrails
• Implement MCP-style integrations to connect agents to enterprise tools/services with strong controls, auditability, and observability
• Deliver AI-enabled business UI experiences in partnership with product and UX, ensure usability, performance, and accessibility
• Design and develop Python and Java services (microservices and shared libraries) with strong API contracts and domain-driven design where applicable
• Partner with platform engineering to build/enhance core capabilities: Shell/component frameworks and reusable UI building blocks ;Resolver servers and orchestration backends ;Gateway services for routing, resiliency, and authN/authZ integration ;OPA-based policy enforcement and policy-as-code enablement
• Own end-to-end delivery, requirements, architecture, implementation, testing, CI/CD, deployment, monitoring, and production support
• Establish and uphold engineering standards for code quality, automated testing, performance tuning, observability (logs/metrics/traces), and resiliency
• Collaborate with security, risk, and controls partners to ensure solutions meet governance and compliance expectations for AI-enabled systems
• Produce reference architectures, templates, and paved paths to accelerate adoption across teams

Required Qualifications, Capabilities, and Skills

• 10+ years of hands-on software engineering experience delivering production-grade systems 
• Strong proficiency in Python and Java, including clean architecture, design patterns, and performance-minded development
• Proven experience building distributed systems/microservices, including REST/gRPC API design and service decomposition
• Hands-on experience with orchestration/workflow patterns (state machines, job runners, event-driven services, or equivalent)
• Strong grounding in secure engineering practices authentication/authorization, secrets handling, least privilege, secure coding
• Experience with policy enforcement/authorization patterns, familiarity with OPA (or similar policy-as-code frameworks)
• Hands-on experience with Elasticsearch for building search, indexing, and analytics capabilities at scale
• Experience designing and implementing Spring Batch jobs for large-scale data processing and ETL workflows
• Solid SDLC discipline, code reviews, unit/integration testing, CI/CD, release hygiene, and production support ownership
• Strong communication and collaboration skills across product, UX, and multiple engineering teams

Preferred Qualifications, Capabilities, and Skills

• Experience building LLM/GenAI applications, including prompt/tool design, RAG patterns, evaluation approaches, and safety controls
• Familiarity with Model Context Protocol (MCP) concepts and building tool ecosystems for agent platforms
• Experience with React/TypeScript and enterprise UI shell/component frameworks
• Experience with Kafka/event streaming and asynchronous, event-driven architectures
• Cloud-native experience(AWS) with containers/Kubernetes and operational excellence (monitoring, alerting, incident response)
  • Background delivering platforms in regulated environments with strong risk and control requirements