Summary
✨ AI‑Generated
Join a quantitative technology team where rigorous mathematics meets strong software engineering. You’ll build AI/ML-driven systems that transform multimodal data into actionable decisions, working independently across the boundary between research and production. The role is suited to an engineer with excellent mathematical and statistical foundations and hands-on experience delivering AI/ML products at scale.
Highlights
Work at the intersection of mathematical research, software engineering, and applied AI/ML. The role offers substantial independence, enterprise-scale engineering challenges, and the opportunity to turn advanced quantitative methods into production systems used by clients across finance and heavy industry.
Description
COMPANY OVERVIEW
At ARRAY, we’re not just another software services company – we’re a team of dreamers, innovators, and trailblazers! From startup grit to big-tech aspirations, we’re on a mission to redefine technology, put Bahrain on the global tech map, and grow into a powerhouse that inspires.
If you’re ready to be part of an exciting journey, we want you on our team!
THE TEAM
Quant Dev combines mathematical rigour and engineering excellence.
The team builds great products at enterprise scale, using AI to turn multi-modal data into impactful decisions.
We come largely from banks, asset managers and applied science backgrounds, and we work where research meets production: applied, deployed and in the hands of clients across finance and heavy industry.
THE ROLE
We’re looking for a strong software engineer – not merely a coder – whose mathematical and statistical foundations are first-rate, and who has real experience building AI/ML-based products.
You’ll work independently on challenging problems, but as part of a close team that ships products.
This is an applied role: working closely with R&D, but measured by what reaches production and what clients use.
KEY RESPONSIBILITIES
Product Engineering: Design, build, deploy and operate production-grade software at enterprise scale, owning features end to end.Applied AI/ML: Own the full machine learning life cycle – data exploration and preprocessing, model selection and training, evaluation, deployment, monitoring and optimisation – inside deployed products, not research notebooks.Data at Scale: Build pipelines and platforms that turn large, multi-modal data (videos, documents, time series) into decision-ready outputs.Analytics Design: Propose and build data analytics that drive decision-making for clients and products.Product Ideation: Contribute to the ideation and shaping of AI-powered products.Engineering Standards: Raise the bar through code review, testing, sound design and expert use of AI coding agents – help the team build faster and safer.
MUST-HAVE SKILLS
Master’s or Ph.D.
in Computer Science, Data Science, Mathematics, Artificial Intelligence or a related quantitative field.Minimum of 5 years of hands-on experience building and deploying software products, with substantial experience developing and deploying AI/ML models in production.Software engineering excellence: system design, testing, version control, code review and CI/CD – beyond modelling code.Strong mathematical and statistical foundations (probability, statistics, linear algebra, optimisation) – able to reason about model behaviour, not just invoke libraries.Strong programming skills in Python and SQL.Proficient in AI and machine learning libraries and frameworks (e.g., PyTorch, scikit-learn) and modern AI tooling.Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying AI and machine learning applications.Independent problem-solver and genuine team player, with excellent verbal and written communication skills.
NICE-TO-HAVE SKILLS
Background in financial services – banks, asset managers, or fintech.Experience working with large-scale data and intelligence platforms.Applied generative AI experience: LLM-powered features, retrieval, agents or fine-tuning in production.Cloud certifications demonstrating expertise in cloud-based AI and ML.Exposure to client-facing roles and understanding client needs in the context of AI and ML solutions.Experience with startup culture – adaptability to a fast-paced, ever-changing environment.