About the role

Lead AI solution design and development.
Build scalable backend services and APIs.
Design data pipelines for large datasets.
Develop and deploy machine learning models.
Collaborate with engineering and data teams.

Skills the posting asks for

AI
Machine Learning
Data Engineering

Summarised from the employer’s posting, which is reproduced in full below.

The employer’s full posting

Description

Overview

We have an immediate need for an Artificial Intelligence (AI) Engineer to support TO-005, Report Authoring and Dissemination (RAD). This role will work closely with system and software engineers to design, prototype, and integrate AI-driven capabilities into the existing RAD architecture—while also contributing to the design of next-generation architecture built for scalability and large data processing.

This is a transformative opportunity to build systems from the ground up that augment human intelligence, streamline workflows, and enable data-driven decision-making across enterprise environments handling high-volume, complex datasets.

Key Responsibilities

AI Solution Design & Development

Lead end-to-end design and development of AI/ML solutions—from concept, prototyping, and architecture design to production deployment
Write production-grade code and contribute to scalable, maintainable software systems
Design modular, extensible architectures that support AI integration within enterprise platforms

Software Architecture & Engineering

Contribute to or lead the design of enterprise-grade software architecture from scratch, including microservices and distributed systems
Build backend services and APIs to support AI-driven applications and data pipelines
Ensure systems are designed for scalability, fault tolerance, and high availability
Implement best practices in software engineering, version control, CI/CD, and testing frameworks

Data Engineering & Large-Scale Processing

Design and implement data pipelines to ingest, process, and analyze large structured and unstructured datasets
Perform Exploratory Data Analysis (EDA) to inform model design and data strategy
Optimize data storage and retrieval for performance and scalability

Model Development & Deployment

Develop, train, evaluate, and fine-tune machine learning and deep learning models
Implement robust validation, testing, and monitoring to ensure model accuracy, fairness, and reliability
Deploy models into production environments using MLOps best practices

Collaboration & Communication

Serve as a technical liaison across engineering, data, and mission stakeholders
Clearly communicate AI approaches, tradeoffs, and system design decisions to both technical and non-technical audiences

Continuous Innovation

Stay current with emerging AI/ML technologies, frameworks, and enterprise data solutions
Identify opportunities to enhance system performance, automation, and intelligence capabilities