As
an AI Engineer, this hands-on technical role builds, integrates
and ships the AI services that power the organization's multi-year AI Workforce
Transformation. Working under the direction of the Senior Manager,
AI Engineering and the Principal AI Engineers, the role implements components
of the AI platform – from orchestration workflows to LLM Gateway integrations –
embedding AI into key workflows.
The
position requires solid software-engineering fundamentals and a working
knowledge of modern AI/ML tools. It exists to turn architectural designs and
standards into reliable, production-ready AI capabilities that elevate
operational productivity and augment human work, leveraging unique
datasets.
Requirements
Feature Development
Build
and integrate AI services, APIs, pipelines and copilots to specification,
writing clean, tested, production-ready code.
Platform Integration
Implement
LLM Gateway and Model Context Protocol (MCP) integrations to connect AI
services with enterprise systems (Jira, Confluence, core banking) under senior
direction.
Data Preparation
Prepare,
clean, label and version datasets and embeddings; apply PII handling and
data-quality checks before data reaches models.
Automated Testing
Write
and run automated tests for AI components – including edge cases, usability and
reliability – and help maintain a robust log of test cases and results.
Monitoring & Observability
Instrument
AI services with logging and metrics (Grafana, ELK) and monitor latency,
throughput, error rates and model performance, raising alerts on anomalies.
Controls Execution
Apply
security, privacy and responsible-AI controls (encryption, PII scrubbing, audit
logging, bias checks) per standards, and escalate risks to senior engineers.
MLOps Support
Support
MLOps / DevSecOps pipelines for model build, test, retraining and deployment,
using infrastructure-as-code and integrated security scans.
Documentation
Document
components, configurations and known limitations so that work is traceable and
maintainable.
Code Review & Collaboration
Participate
in code reviews, pair programming and knowledge sharing; make efficient use of
source control and issue-tracking tools.
Continuous Improvement
Works
well within an agile team, communicates clearly, and supports peers through
reviews and knowledge sharing.
Education & Work Experience
A
Bachelor’s degree in Computer Science, Software Engineering or related field (a
Master’s degree in AI/ML or Data Science is a plus), with 2–4 years’
software-engineering experience including hands-on exposure to AI/ML or
data-intensive applications.
Software Engineering Fundamentals
Strong
programming skills (e.g. Python, plus Java / Kotlin / C# or similar), a good
grasp of data structures, APIs (REST/GraphQL) and version control (Git); writes
clean, testable, maintainable code.
AI/ML Tools & LLMs
Working
knowledge of AI/ML concepts and Large Language Models – prompt design, model
APIs, embeddings, and vector databases (e.g. Pinecone, Weaviate, FAISS) for
semantic search and retrieval.
Cloud & Containerisation
Practical
experience deploying services on AWS and/or Azure using Docker; familiarity
with Kubernetes and CI/CD pipelines (GitLab CI, Jenkins or GitHub Actions).
Data Handling & Privacy
Experience
preparing, cleaning and versioning datasets; understanding of PII handling,
dataquality checks and basic privacy/security practices for AI pipelines.
Testing & Observability
Skilled
in automated testing (unit and integration, including edge cases) and
instrumenting services with logging and metrics (e.g. ELK, Grafana).
Responsible AI Awareness
Awareness
of explainability, bias and responsible-AI principles, with the willingness to
apply controls and standards in day-to-day delivery.
Collaboration
Effective
team player in an agile environment with good communication skills and the
ability to manage competing priorities.
Certifications
