Senior Manager, AI Engineering

As the Senior Manager, AI Engineering, this role provides enterprise-wide architectural and people leadership for the AI platform and the multi-year AI Workforce Transformation. Beyond owning the end-to-end AI architecture from the AI Orchestration Layer to a unified LLM Gateway, the role sets the organisation-wide AI architecture strategy, standards and governance, and leads the AI Engineering team (Principal AI Engineers, AI Engineers and the AI Adoption & Enablement Lead).

The position requires authoritative expertise in modern AI/ML platforms and enterprise architecture, operating at a principal level. It exists to elevate operational productivity through data-centric AI enablement at scale, leveraging unique datasets to build proprietary AI solutions that augment human capabilities across every domain.

Requirements

Technical Competencies

AI Strategy & Team Leadership

Set and own the enterprise AI architecture strategy, target-state blueprint and standards; lead, mentor and grow the AI Engineering team (Principal AI Engineers, AI Engineers and the AI Adoption & Enablement Lead).

AI Platform Delivery

Direct the design and delivery of the end-to-end AI platform – AI Orchestration Layer, Unified LLM Gateway, vector stores and MCP integrations – on containerised microservices and Kubernetes (EKS/AKS).

Model Risk & Governance

Chair the model-risk and AI governance forums; ensure each model (especially in credit and fraud) undergoes independent validation, bias testing and stress-testing with formal sign-off before deployment.

Standards Compliance

Own organisation-wide conformance to ISO 42001 and the NIST AI Risk Management Framework, translating standards into enforceable internal policies for explainability, monitoring and periodic risk assessment.

Human-in-Loop to AI-in-Loop Transition

Approve the Human-in-the-Loop to AI-in-the-Loop transition – define criteria (accuracy ≥95%, high user trust, zero compliance issues) and hold authority to approve, halt or revert systems.

Vendor Due Diligence & Budget

Lead technical due diligence and approval of third-party AI tools and

cloud services (SOC 2, encryption, zero data retention) with Procurement and InfoSec, and own AI platform budget.

Privacy by Design

Establish privacy-by-design across the platform – PII scrubbing through the AI Gateway and clear labelling of AI-generated outputs.

Orchestration & Tooling Standards

Set standards for AI orchestration platforms (e.g. LangChain), LLM gateways across providers (OpenAI, Anthropic, HuggingFace) and vector databases (Pinecone, Weaviate, FAISS).

MLOps / DevSecOps

Oversee enterprise MLOps / DevSecOps pipelines (Jenkins, GitLab CI/CD, GitHub Actions, Terraform/CloudFormation) with integrated SAST/DAST security scanning.

Monitoring & Observability

Establish monitoring and observability standards (Grafana, ELK,

PagerDuty) for response times, throughput, error rates and token usage.

Education & Experience

A Bachelor’s degree in Computer Science, Software Engineering or related field (a Master’s degree in AI/ML or Data Science is strongly preferred), with 12+ years in software engineering or architecture – including at least 6 years designing and leading AI, data or cloud architectures at scale, with demonstrable enterprise / transformation leadership and peoplemanagement experience.

Leadership & Governance Track Record

Proven experience leading architecture teams, chairing governance / model-risk forums, and influencing executive and board stakeholders.

AI Platform & ML Architecture

In-depth knowledge of AI/ML solution design – Large Language Models (LLMs), multi-model orchestration, agent frameworks, and vector databases for embedding storage and semantic search.

Cloud-Native Engineering

Authoritative cloud-native engineering on AWS and/or Azure using Docker and Kubernetes; familiarity with hybrid-cloud / on-premises integration for sensitive workloads.

MLOps, CI/CD & Observability

Deep MLOps and DevOps mastery – CI/CD pipelines for model deployment, and observability with ELK and Grafana (latency, drift, accuracy).

API Management & Secure Gateway Design

Expertise in API gateway and secure LLM-gateway design – centralised key management, request logging, throttling, JWT/OAuth, rate limiting and multi-tenant management.

Enterprise Integration (MCP & Connectors)

Enterprise integration experience using Model Context Protocol (MCP) or similar patterns to fetch enterprise data in a governed way.

Data Governance, Privacy & Responsible AI

Strong data governance, privacy engineering, explainable and responsible AI expertise (SHAP/LIME, fairness and bias evaluation) aligned to ISO 42001 and the NIST AI Risk Management Framework.

Security & Compliance

Strong security and compliance grounding – SOC 2, encryption in transit and at rest, zerodata-retention enforcement, and vendor risk assessment.

Certifications