Senior Engineer, Platform Engineering and Architecture

Role Overview

Senior Engineer, Platform Engineering and Architecture at First Abu Dhabi Bank (FAB), based in Abu Dhabi. Lead the engineering, architecture, and run-state ownership of the bank's enterprise AI & Agentic Platform, ensuring it operates as a reliable, secure, observable, and bank-grade production platform supporting agents and AI workloads across the Group.

Role Purpose

To serve as the senior technical authority for platform engineering and architecture across the agentic runtime, model gateway, integration fabric, identity layer, and infrastructure backbone of the platform. This deeply technical role operates in a two-in-a-box model with the existing Platform Product Owner to provide concurrent technical ownership and organizational resilience, with shared accountability for platform availability, performance, cost, security posture, and architectural evolution.

Key Responsibilities

Platform Architecture

• Own the end-to-end technical architecture of the AI & Agentic Platform's five-layer stack: Action Gateway, Agent Kernel, Control Plane, Knowledge Foundation, and Users and Channels layer.
• Architect and evolve the agentic runtime to natively support the emerging multi-protocol stack: Model Context Protocol (MCP) for agent-to-tool access, Agent-to-Agent (A2A) for inter-agent coordination and task delegation, and Agent Communication Protocol (ACP) and equivalent emerging standards.
• Ensure the platform is interoperable with hyperscaler agent fabrics including Azure AI Foundry agents, AWS Bedrock Agents, and Google ADK.
• Design the platform's agent kernel, including agent lifecycle management, planning and reasoning loops, memory architecture (short-term, long-term, episodic), state management, session affinity, scratchpad persistence, and execution chains.
• Architect the platform's knowledge foundation, including vector store selection and topology, hybrid retrieval (BM25, dense, graph), embeddings strategy, knowledge graph integration (FIBO, OWL, SHACL, SPARQL), context engineering, and grounding patterns.
• Design the platform's control plane, including policy-gated execution, Know Your Agent (KYA) enforcement at runtime, agent registries, tool registries, capability discovery, evaluation pipelines, and tracing and lineage at agent and tool granularity.

Integrations & Model Layer

• Architect and engineer the platform's LLM gateway and model abstraction layer, providing a unified interface across foundation model providers: Azure AI Foundry, AWS Bedrock, OpenAI, Anthropic, Google Vertex AI, and Cohere.
• Implement intelligent routing, fallback, retries, prompt and response caching, semantic caching, rate limiting, token accounting, cost attribution, and tenant isolation.
• Design model serving patterns for managed APIs, dedicated capacity (PTUs / provisioned throughput), and self-hosted open-weight models on GPU infrastructure (vLLM, TGI, Triton, or equivalent), with appropriate trade-offs across cost, latency, sovereignty, and compliance.
• Lead the integration architecture between the AI Platform and the bank's core estate, including core banking, payments, treasury, credit and risk systems, the enterprise data platform (Azure, Cloudera, Databricks), enterprise APIs, ESB, and event streaming (Kafka, Event Hubs).
• Integrate with the data product layer.
• Design the action gateway as the bank's enforcement boundary for agentic action, including API mediation, contract enforcement, circuit breakers, idempotency guarantees, transactional safety, and audit-grade action logging.
• Engineer the platform's tool layer and MCP server estate, including tool packaging, versioning, capability advertisement, schema enforcement, and runtime tool discovery across Wholesale, Retail, and Group functions.

Identity, Security & Governance

• Architect and own the agent identity and workload identity model for the platform, including non-human identity (NHI) management, agent identity lifecycle, blended user-plus-agent identity for delegated actions, and the identity propagation chain across multi-agent flows.
• Design the platform's authentication and authorization architecture, including OAuth 2.1 and OIDC flows for agent-to-tool and agent-to-API interactions.
• Implement just-in-time credential issuance, short-lived token exchange, mTLS for agent-to-agent communication, and integration with the bank's enterprise IAM (Microsoft Entra ID, PAM, secrets management).
• Implement zero-trust principles across the agentic stack, including least-privilege scoping per agent and per task, real-time policy evaluation, behavioral posture checks, and continuous authorization rather than static service-account-style access.
• Engineer the platform's runtime governance controls, including KYA enforcement, prompt and output guardrails (PII, PHI, MNPI, prompt injection defense), sensitive action approval flows, and human-in-the-loop escalation patterns.
• Harden the platform to meet CBUAE, internal model risk, and Group governance requirements, including auditability, lineage, data residency, model risk controls, third-party model governance, and OWASP LLM Top 10 alignment.

Infrastructure & Run-State

• Lead the platform's cloud and infrastructure architecture across Azure (primary) and AWS, including infrastructure as code (Terraform), networking (private endpoints, peering, egress control), Kubernetes (AKS) and container orchestration, secrets management, and CI/CD pipelines.
• Own platform Site Reliability Engineering (SRE), including SLO design, error budget management, observability (OpenTelemetry, traces, metrics, logs at agent and tool granularity), incident response, and post-mortems.
• Lead capacity planning and cost governance for a growing fleet of agents and AI workloads in production.
• Operate in genuine two-in-a-box with the existing Platform Product Owner, including shared on-call, shared roadmap ownership, and shared accountability for major architectural decisions, regulator conversations, and critical incidents.
• Drive engineering excellence across the platform team, including testing discipline (unit, integration, evaluation, red-teaming), documentation, infrastructure as code maturity, operational readiness reviews, and technical mentorship of platform engineers.
• Represent the platform in senior technical forums with Enterprise Architecture, Cyber, Model Risk, Internal Audit, and the Group CTTO's office on architecture and engineering matters.
• Engage with hyperscale, model provider, and framework vendor technical teams on platform-level integration, performance, sovereignty, and cost optimization.

Qualifications & Experience

Technical Expertise

• Deep, current expertise in agentic AI architecture and the modern multi-protocol stack: Model Context Protocol (MCP), Agent-to-Agent (A2A), Agent Communication Protocol (ACP), and the engineering patterns required to build interoperable agentic systems on top of them at platform scale.
• Strong hands-on knowledge of agent orchestration frameworks: LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, OpenAI Agents SDK, or equivalent.
• Strong hands-on knowledge of planning and reasoning loops, multi-agent coordination patterns, agent memory systems, and evaluation harnesses (RAGAS, OPIK, LangSmith, Promptfoo, or equivalent).
• Deep technical knowledge of LLM serving and inference architecture, including managed model APIs (Azure AI Foundry, AWS Bedrock, OpenAI, Anthropic, Google Vertex AI), provisioned throughput / PTUs, and self-hosted open-weight models.
• Deep technical knowledge of GPU scheduling, vLLM / TGI / Triton, KV-cache management, batching strategies, and the cost-latency-quality trade-offs that drive model selection.
• Strong working expertise in LLM gateway and AI gateway architecture, including model routing, fallback and retry, prompt and semantic caching, rate limiting, tenant isolation, token accounting, and policy enforcement at the gateway boundary.
• Deep experience in retrieval and knowledge architecture, including vector databases (pgvector, Azure AI Search, Pinecone, Weaviate, Qdrant, or equivalent), hybrid retrieval, and reranking.
• Deep experience in embeddings model selection, knowledge graphs (FIBO, RDF, OWL, SHACL, Neo4j, Apache Jena), and context engineering at scale.
• Strong grounding in agent and workload identity, including non-human identity (NHI) governance, agent identity lifecycle, OAuth 2.1, OIDC, SPIFFE / SPIRE-style workload identity, and mTLS.
• Strong grounding in just-in-time credentialing, secret-less architectures, and integration with enterprise IAM (Microsoft Entra ID, Azure AD, PAM platforms).
• Deep proficiency in cloud-native platform engineering on Azure (preferred) and AWS, including