As a Data Assurance Lead, you will implement a Data Assurance Framework, automate data contracts, and ensure data quality in complex Lakehouse environments, particularly for financial services clients.

The AWS Glue Data Engineer will migrate source systems to the Lakehouse, build and optimize ingestion pipelines, and ensure data quality and compliance, collaborating closely with various teams.

The Project Manager will lead Wealth Management initiatives, ensuring timely delivery of AI solutions while managing cross-functional teams, budgets, and risks.

Serve as the bridge between business and technical teams to deliver AI solutions: elicit and document requirements, validate AI use cases with data scientists, define model test strategies, support UAT and change management, produce training materials, identify automation opportunities, and ensure regulatory, ethical, and governance compliance throughout project lifecycles.

Lead design, deployment, and operations of scalable GenAI infrastructure on Azure. Build IaC with Terraform, manage AKS/ACR, CI/CD and MLflow pipelines, implement secure networking and secret management, design data pipelines with ADF/Databricks/Synapse, monitor models and costs, and mentor team members to deliver compliant AI solutions for banking clients.

Design, build, and scale enterprise-grade AI systems including LLM-based conversational agents, RAG pipelines, embedding services, and automation agents for banking. Implement MLOps, CI/CD, containerized deployments, model optimization, observability, and governance while translating technical solutions to business stakeholders.

Lead definition and delivery of enterprise Generative AI platforms. Translate strategy into roadmaps, own AI product lifecycle, act as Product Owner for AI squads, define evaluation frameworks, liaise between technical teams and business stakeholders, and ensure compliance, security, and measurable ROI in a regulated banking environment.

Serve as the bridge between business and technical teams to define AI requirements, validate use-case feasibility, design model test strategies (including bias detection), support UAT and change management, create user training, identify automation opportunities in banking workflows, and mentor junior staff to ensure compliant, auditable, and value-driven AI deployments.

Lead design, deployment, and management of scalable MLOps and LLMOps solutions across AWS and Azure. Build CI/CD pipelines, provision infrastructure with Terraform, containerize and orchestrate workloads, deploy and monitor models, enforce cloud security and compliance, design data pipelines, and mentor junior engineers while liaising with stakeholders to translate technical decisions into business value.

Translate complex banking business needs into actionable AI requirements, validate feasibility with data science teams, define model test strategies (validation, bias detection), support UAT and change management, create training materials, identify automation opportunities, maintain audit trails, and mentor junior staff to ensure ethical, compliant, and value-driven AI deployments.

Design and scale enterprise GenAI solutions (LLMs, RAG, agents) for banking; select stacks, produce architecture documentation, coach engineering squads, prototype multimodal/agentic systems, and ensure security and compliance.

Lead design, deployment, and management of scalable AI infrastructure on Azure. Build AKS/VM environments, CI/CD and MLOps pipelines, IaC with Terraform, secure networking and secrets management, data pipelines with ADF/Databricks/Synapse, and monitor model performance and costs while leading and mentoring the Cloud Ops team.

Serve as the bridge between business and technical teams to translate business needs into AI requirements, validate feasibility, define testing/validation strategies, support UAT and change management, document audit trails/versioning, and identify automation opportunities within banking workflows.

Design and implement automated SRE and AI QA frameworks to validate conversational AI, RAG pipelines, and banking microservices. Integrate reliability testing into CI/CD for MLOps, perform load and failover testing across Azure and AWS, and enforce data integrity, fairness, and compliance. Act as technical liaison translating reliability requirements into business-facing quality narratives.

Design, deploy, and operate scalable MLOps/LLMOps infrastructure across AWS and Azure. Build CI/CD pipelines, deploy models (SageMaker, Azure ML, EKS), implement monitoring and drift detection, create data pipelines, enforce cloud security/compliance, and lead team activities.

Design, build, and deploy scalable, cloud-native AI-powered backend systems for banking. Integrate LLMs and RAG workflows, create data pipelines, manage containerized CI/CD deployments, and translate business requirements into secure, modular architectures aligned with compliance and stakeholder needs.

The Data Engineer designs and optimizes scalable data solutions, manages data pipelines, and implements MLOps principles to support AI systems and enhance data quality and governance.

Lead complex AI and data projects from inception to deployment, managing cross-functional teams and ensuring delivery within scope, budget, and timeline.

The BI/Analytics Reporting Specialist will design, develop, and maintain analytical reports and dashboards using MicroStrategy, manage the BI environment, and ensure optimal platform use and security.

The DataOps Engineer will oversee the Lakehouse environment, managing incidents, monitoring data pipelines, conducting root cause analysis, and ensuring data quality and operational integrity.