Senior AI and Machine Learning Engineer
Summary
Build and deploy AI/ML systems for financial services, including LLMs, RAG pipelines, and agentic workflows using Python, cloud platforms, and MLOps practices.
Snr AI & ML Engineer
Location: Abu Dhabi, Abu Dhabi, United Arab Emirates
Department: Weekday's Client via platform
Workplace: on_site
Employment Type: full
Description
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฐ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฌ-๐ฐ๐ฌ ๐๐ฃ๐)
Experience: 7+ yrs
Location: Abu Dhabi, United Arab Emirates
Job Type: Full-time
We are seeking a Senior AI/ML Engineer to design, develop, and deploy production-grade AI and machine learning solutions for financial services use cases. This is a hands-on role for an experienced professional with strong expertise in Python, Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and Data Engineering.
The ideal candidate will be comfortable transforming complex business and financial requirements into scalable AI-powered applications. You will work closely with finance, quantitative, technology, and business teams to develop practical solutions across areas such as risk, credit, compliance, treasury, markets, and financial reporting.
Requirements
Key Responsibilities
- Design, develop, and deploy AI/ML and Generative AI solutions for financial services use cases.
- Build LLM applications, RAG pipelines, AI agents, document intelligence solutions, and workflow automation tools.
- Develop scalable Python-based APIs, model services, dashboards, and data pipelines.
- Work with financial datasets, including transaction data, credit data, market data, financial statements, and unstructured documents.
- Support AI solutions across risk management, credit, treasury, compliance, markets, corporate banking, and management reporting.
- Prototype innovative AI solutions rapidly and transform successful prototypes into production-grade applications.
- Design and implement solutions using technologies such as Python, pandas, NumPy, scikit-learn, PyTorch, TensorFlow, FastAPI, Flask, Streamlit, LangChain, LangGraph, LlamaIndex, vector databases, embeddings, and RAG.
- Build and integrate cloud-native solutions using Azure, AWS, or GCP, along with Docker, Kubernetes, Git, and CI/CD practices.
- Collaborate with finance and quantitative teams to translate complex methodologies into practical software solutions.
- Implement appropriate testing, logging, monitoring, security, and basic MLOps practices.
- Work with cross-functional teams to understand business requirements and deliver scalable solutions.
- Mentor junior engineers, developers, and analysts on coding standards, modelling practices, testing, and deployment.
What Makes You a Great Fit
- 7+ years of experience in AI/ML Engineering, Data Science, Software Engineering, or Analytics Engineering.
- Strong hands-on expertise in Python and modern machine learning frameworks.
- Proven experience building and deploying real-world AI/ML applications rather than working exclusively with notebooks or prototypes.
- Strong understanding of LLMs, Generative AI, RAG, agentic workflows, NLP, embeddings, vector databases, or document intelligence.
- Experience developing APIs, data pipelines, dashboards, and production-grade model services.
- Familiarity with SQL, structured databases, cloud platforms, containers, CI/CD, testing, and deployment practices.
- Strong problem-solving and analytical skills with the ability to work through ambiguous business challenges.
- Ability to communicate effectively with both technical teams and finance/business stakeholders.
- Exposure to banking, fintech, payments, insurance, asset management, consulting, or capital markets is highly valuable.
- Knowledge of financial use cases such as credit risk, fraud, KYC, treasury, trading, portfolio analytics, regulatory reporting, or financial document processing is an advantage.
- A practical, delivery-focused mindset with the ability to build solutions quickly and continuously improve them for production use.