Asks for

The ad says“15 or more years of experience spanning data engineering, software engineering, and AI/ML systems, with at least 3 to 5 years in a senior architecture role”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“Data Engineering and Platform: Python (primary), SQL, Scala, Bash; Apache Spark (PySpark), AWS Glue, dbt, Pandas, Polars; Apache Kafka (AWS MSK), AWS Kinesis, AWS EventBridge, Flink; Delta Lake, Apache Iceberg, AWS S3, Databricks (Unity Catalog); AWS Redshift, Databricks SQL, Snowflake; Feast, AWS SageMaker Feature Store, Tecton; Great Expectations, dbt tests, Soda Core; OpenMetadata, Apache Atlas; Metabase, Redash; Apache Airflow (Astronomer), Dagster, Prefect”
The ad says“Strong software engineering discipline with production-quality Python and SQL, and solid understanding of distributed systems and API design”
The ad says“We are seeking a Senior AI Architect to design, build, and scale the AI and data platform that the organization's AI products will run on. This is a hands-on, founding technical position. The Senior AI Architect will produce architecture designs, write production code, build data pipelines, and set the engineering standards the broader AI team will follow. The role requires rare technical breadth: deep data engineering foundations, strong software engineering discipline, AI and ML systems experience, cloud infrastructure fluency, and the architectural vision to make it all coherent at enterprise scale.”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“Data Engineering and Platform: Python (primary), SQL, Scala, Bash; Apache Spark (PySpark), AWS Glue, dbt, Pandas, Polars; Apache Kafka (AWS MSK), AWS Kinesis, AWS EventBridge, Flink; Delta Lake, Apache Iceberg, AWS S3, Databricks (Unity Catalog); AWS Redshift, Databricks SQL, Snowflake; Feast, AWS SageMaker Feature Store, Tecton; Great Expectations, dbt tests, Soda Core; OpenMetadata, Apache Atlas; Metabase, Redash; Apache Airflow (Astronomer), Dagster, Prefect”
The ad says“Build and own the MLOps platform covering experiment tracking, training pipeline automation, model packaging and deployment standards, CI/CD for ML, and model monitoring with drift detection”
The ad says“Security-conscious approach as a default, with experience embedding data privacy and compliance requirements into architecture”
The ad says“15 or more years of experience spanning data engineering, software engineering, and AI/ML systems, with at least 3 to 5 years in a senior architecture role”
The ad says“Security-conscious approach as a default, with experience embedding data privacy and compliance requirements into architecture”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“Strong software engineering discipline with production-quality Python and SQL, and solid understanding of distributed systems and API design”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“API, Integration and Solution Architecture: FastAPI, gRPC, REST (OpenAPI 3.1), GraphQL; Kong, AWS API Gateway, Istio (service mesh, mTLS); Docker, Kubernetes (Helm, Kustomize); C4 Model (Structurizr), ADRs, draw.io, Lucidchart; AWS Cost Explorer, Grafana cost dashboards, per-product token attribution”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“Build and own the MLOps platform covering experiment tracking, training pipeline automation, model packaging and deployment standards, CI/CD for ML, and model monitoring with drift detection”
The ad says“Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka”
The ad says“Data Engineering and Platform: Python (primary), SQL, Scala, Bash; Apache Spark (PySpark), AWS Glue, dbt, Pandas, Polars; Apache Kafka (AWS MSK), AWS Kinesis, AWS EventBridge, Flink; Delta Lake, Apache Iceberg, AWS S3, Databricks (Unity Catalog); AWS Redshift, Databricks SQL, Snowflake; Feast, AWS SageMaker Feature Store, Tecton; Great Expectations, dbt tests, Soda Core; OpenMetadata, Apache Atlas; Metabase, Redash; Apache Airflow (Astronomer), Dagster, Prefect”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“Data Engineering and Platform: Python (primary), SQL, Scala, Bash; Apache Spark (PySpark), AWS Glue, dbt, Pandas, Polars; Apache Kafka (AWS MSK), AWS Kinesis, AWS EventBridge, Flink; Delta Lake, Apache Iceberg, AWS S3, Databricks (Unity Catalog); AWS Redshift, Databricks SQL, Snowflake; Feast, AWS SageMaker Feature Store, Tecton; Great Expectations, dbt tests, Soda Core; OpenMetadata, Apache Atlas; Metabase, Redash; Apache Airflow (Astronomer), Dagster, Prefect”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“15 or more years of experience spanning data engineering, software engineering, and AI/ML systems, with at least 3 to 5 years in a senior architecture role”
The ad says“Strong software engineering discipline with production-quality Python and SQL, and solid understanding of distributed systems and API design”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”
The ad says“Cloud and Infrastructure (AWS Primary): EKS (Karpenter), EC2 (G5, P4d GPU instances), SageMaker, Lambda, Fargate; S3, FSx for Lustre, ElastiCache (Redis), DynamoDB, RDS (PostgreSQL); SageMaker Pipelines, Feature Store, Model Registry, Bedrock, Comprehend, Textract; VPC, Transit Gateway, PrivateLink, Route 53, CloudFront, WAF; IAM (IRSA), Secrets Manager, KMS, Macie, GuardDuty, Security Hub, CloudTrail; Terraform, Terragrunt, CloudFormation, Pulumi; ArgoCD, Flux, Atlantis; Azure ML, Azure Databricks, Azure OpenAI Service (secondary)”
The ad says“AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)”

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.

Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.

Role Overview

We are seeking a Senior AI Architect to design, build, and scale the AI and data platform that the organization's AI products will run on. This is a hands-on, founding technical position. The Senior AI Architect will produce architecture designs, write production code, build data pipelines, and set the engineering standards the broader AI team will follow. The role requires rare technical breadth: deep data engineering foundations, strong software engineering discipline, AI and ML systems experience, cloud infrastructure fluency, and the architectural vision to make it all coherent at enterprise scale.

Key Responsibilities

Design and build the end-to-end AI platform architecture, covering data ingestion, feature engineering, model training, serving, monitoring, and retraining as a coherent, maintainable production system
Design the data lakehouse architecture on AWS using Delta Lake or Apache Iceberg with Databricks as the primary compute layer, and build batch and streaming data pipelines using PySpark, AWS Glue, and Kafka
Architect and implement a feature store with online and offline stores, point-in-time joins, and feature versioning
Own cloud infrastructure architecture for all AI and data workloads on AWS, including multi-account strategy, EKS cluster design, GPU compute, storage, and cost governance
Build and own the MLOps platform covering experiment tracking, training pipeline automation, model packaging and deployment standards, CI/CD for ML, and model monitoring with drift detection
Design the AI services layer, including reusable inference APIs, model serving infrastructure, and API gateway configuration with authentication, rate limiting, and cost attribution
Integrate AI capabilities into the organization's products and business systems using event-driven and API-based patterns
Embed security and compliance into every layer of the AI platform, including network security, IAM, PII handling, secrets management, and audit logging
Act as the senior technical authority for the AI Initiative, setting engineering standards, mentoring engineers, and leading technical decisions
Produce and maintain architecture documentation including C4 diagrams, Architecture Decision Records, and system integration maps

Requirements

15 or more years of experience spanning data engineering, software engineering, and AI/ML systems, with at least 3 to 5 years in a senior architecture role
Demonstrable record of building and shipping production AI systems end-to-end, not solely designing them
Deep hands-on data engineering background including production data pipelines, data lakehouses, and feature stores
Expert-level AWS skills across multi-service architecture design and build; GCP or Azure familiarity is beneficial
Strong software engineering discipline with production-quality Python and SQL, and solid understanding of distributed systems and API design
Deep understanding of the AI and ML model lifecycle and the infrastructure required to support it at production scale
Experience in high-growth product companies, scale-ups, or enterprise AI teams where significant technical decisions were owned
Security-conscious approach as a default, with experience embedding data privacy and compliance requirements into architecture
Strong written and verbal communication, including the ability to produce clear architecture documentation and present to senior leadership
Experience leading or significantly contributing to technical team building, including mentoring and setting engineering standards
Fintech experience is preferred

Technical Skills

Data Engineering and Platform: Python (primary), SQL, Scala, Bash; Apache Spark (PySpark), AWS Glue, dbt, Pandas, Polars; Apache Kafka (AWS MSK), AWS Kinesis, AWS EventBridge, Flink; Delta Lake, Apache Iceberg, AWS S3, Databricks (Unity Catalog); AWS Redshift, Databricks SQL, Snowflake; Feast, AWS SageMaker Feature Store, Tecton; Great Expectations, dbt tests, Soda Core; OpenMetadata, Apache Atlas; Metabase, Redash; Apache Airflow (Astronomer), Dagster, Prefect
AI and ML Systems: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM; AWS SageMaker Training, Kubeflow, Ray Train; BentoML, Seldon Core, Ray Serve, FastAPI, KServe; MLflow, Weights and Biases, ClearML; Evidently AI, Arize AI, WhyLabs; GitHub Actions, DVC, Great Expectations; Hugging Face (Transformers, Datasets, Evaluate, PEFT)
Cloud and Infrastructure (AWS Primary): EKS (Karpenter), EC2 (G5, P4d GPU instances), SageMaker, Lambda, Fargate; S3, FSx for Lustre, ElastiCache (Redis), DynamoDB, RDS (PostgreSQL); SageMaker Pipelines, Feature Store, Model Registry, Bedrock, Comprehend, Textract; VPC, Transit Gateway, PrivateLink, Route 53, CloudFront, WAF; IAM (IRSA), Secrets Manager, KMS, Macie, GuardDuty, Security Hub, CloudTrail; Terraform, Terragrunt, CloudFormation, Pulumi; ArgoCD, Flux, Atlantis; Azure ML, Azure Databricks, Azure OpenAI Service (secondary)
API, Integration and Solution Architecture: FastAPI, gRPC, REST (OpenAPI 3.1), GraphQL; Kong, AWS API Gateway, Istio (service mesh, mTLS); Docker, Kubernetes (Helm, Kustomize); C4 Model (Structurizr), ADRs, draw.io, Lucidchart; AWS Cost Explorer, Grafana cost dashboards, per-product token attribution
Third-Party Data Integrations: Segment, Amplitude, Adjust, Firebase, MoEngage, JourneyFi, Funnel, Debezium, AWS DMS, Fivetran
Security and Compliance: OAuth 2.0, JWT, AWS Cognito, mTLS, AWS SSO; AWS Secrets Manager (auto-rotation), HashiCorp Vault; Microsoft Presidio (PII detection), AWS Macie, field-level KMS encryption; NIST AI RMF, GDPR, ISO 27001, SOC 2; Model cards, Fairlearn (bias evaluation), SHAP (explainability), audit logging

Why Join Us?

Work with one of the world’s leading financial derivatives institutions.
Competitive salary plus performance-based incentives.
Access to a dynamic, international, and fast-growing environment.
Strong opportunities for career progression within a global financial group.
Be part of a business committed to innovation, excellence, and long-term growth.

Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.

MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

Asks for

Values