Building and scaling secure agentic AI platforms and microservices, embedding AI into business UI workflows.
As an Applied AI ML lead in Global Banking Technology team , you will be a hands-on full-stack engineer and technical leader responsible for building and scaling agentic AI capabilities—including agents, MCP integrations, and orchestration services—and delivering AI-powered business UI use cases that embed these capabilities into real workflows. In parallel, you will partner closely with platform engineering teams to design and implement foundational platform services such as UI shell components, resolver servers, gateway services, and OPA-based policy enforcement.
You will own solutions end-to-end—from architecture and implementation through CI/CD, production readiness, and operational stability—while setting a high bar for engineering quality, security, resiliency, and developer experience.
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
