Note: The job is a remote job and is open to candidates in USA. Finanshels.com is the UAE's leading tech-enabled accounting and tax platform, and they are seeking an experienced AI Engineer to design and deploy scalable ML models and build robust data pipelines. The role involves owning the full ML lifecycle and collaborating with various teams to develop AI-driven solutions for financial operations.
Responsibilities
- Design, build, and deploy scalable machine learning models to support financial automation, forecasting, anomaly detection, and other intelligent product features
- Architect and maintain robust ETL/ELT data pipelines that reliably move and transform data from diverse financial and operational sources
- Own the full ML lifecycle — from data collection and feature engineering to model training, validation, deployment, and ongoing monitoring in production
- Collaborate closely with data engineers, product managers, and finance domain experts to translate business problems into AI-driven solutions
- Build and maintain infrastructure for model serving, versioning, and CI/CD to ensure reliable, scalable deployment of AI systems
- Monitor deployed models for performance, drift, and data quality issues, and iterate to keep systems accurate and reliable at scale
- Ensure data pipelines and ML systems meet standards for security, compliance, and data privacy appropriate to a regulated financial environment
- Evaluate and integrate relevant AI/ML tools, frameworks, and third-party APIs (including LLMs) to accelerate development where appropriate
- Document architecture, models, and pipelines to support knowledge sharing and long-term maintainability
- Mentor junior engineers and contribute to best practices for AI/ML development within the engineering team
Skills
- ~8 years of overall experience in software/data engineering, with substantial hands-on experience in machine learning and AI system design
- Proven experience designing, training, and deploying machine learning models in production environments at scale
- Strong experience building ETL/ELT data pipelines using modern data engineering tools and practices
- Solid programming skills in Python (and familiarity with SQL); experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience with data pipeline/orchestration tools (e.g., Airflow, dbt, Spark, Kafka, or similar)
- Familiarity with cloud platforms (AWS, GCP, or Azure) and cloud-native data/ML services
- Experience with MLOps practices — model versioning, CI/CD for ML, monitoring, and reproducibility
- Understanding of data warehousing concepts and experience working with large-scale structured and unstructured data
- Strong analytical and problem-solving skills, with the ability to translate ambiguous business needs into technical solutions
- Excellent communication skills and the ability to work cross-functionally with engineering, product, and finance teams
- Experience applying AI/ML in fintech, accounting, or financial services
- Hands-on experience with large language models (LLMs), RAG pipelines, or generative AI applications
- Experience with real-time data processing and streaming architectures
- Familiarity with data governance, security, and compliance requirements in regulated industries
Company Overview