Senior AI Architect

Overview

Role Overview

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.