Jobs at Deeplight AI

The Senior Azure Platform Engineer will architect and deploy secure and automated Azure infrastructures, manage Azure AI services, and ensure cost-effective operations while leading FinTech integration projects.

The Specialist - Vulnerability Management will assess, prioritize, and track vulnerabilities in a banking environment, ensuring compliance with security policies and regulations.

Responsible for designing, developing, and maintaining enterprise-grade Power BI dashboards and reports, translating cybersecurity data into actionable insights for executives within a banking environment.

The Senior Specialist for SOC Threat Intelligence analyzes cyber threats, implements detection capabilities, monitors adversary tactics, and supports incident response within a bank's SOC.

Lead the design and implementation of security controls in banking environments, ensuring compliance and technical integrity across systems while mentoring other engineers.

The role involves managing cloud security vulnerabilities for a banking client, overseeing vulnerability management, collaborating with engineering teams, and producing risk reports.

Lead end-to-end enterprise sales across UAE/GCC while sourcing and coordinating AI/Data/Cloud consulting talent. Manage pipeline, proposals, RFPs, contracts, and post-sale client relationships, plus interview orchestration, onboarding, visa/labor documentation, and HR operations support.

Own end-to-end data platform verification: design and run automated functional, integration, and E2E tests, API/contract testing, source-to-target reconciliation, Soda Core checks across Bronze/Silver/Gold, monitor alerts and perform root-cause analysis, and embed automated validation into live data flows while collaborating with data engineering squads.

Design and implement scalable conceptual, logical, and physical data models and semantic layers for Lakehouse/Medallion architectures. Build knowledge graphs and ontologies, integrate with metadata/lineage tools, ensure schema evolution and performance, and collaborate with architects, engineers and stakeholders to enable self-service analytics and data discovery.

Lead design and implementation of secure, compliant multi‑cloud (Azure/AWS) architectures for AI and banking platforms. Own HLDs/LLDs, cloud governance, FinOps, CI/CD pipeline security, zero‑trust networking, and mentor engineering squads while advising senior stakeholders on architecture and regulatory risk.

Own and automate the AWA platform deployment architecture in Azure: IaC (Terraform/Bicep), AKS cluster and namespace management, CI/CD pipelines, image signing, security policies, monitoring (KQL/Azure Monitor), private connectivity, Key Vault secret management, and automated DR to meet RTO/RPO targets.

Build and maintain human-facing surfaces for the Agentic Workflow Automation platform, including HITL reviewer UI, AgentOps dashboards, internal tooling, and design system. Integrate real-time streams, Azure monitoring, secure APIs, authentication, containerized deployments (AKS), and optimise performance and accessibility for enterprise banking workflows.

Lead delivery of complex AI, data science, and banking projects end-to-end. Combine Agile and traditional methodologies, manage budgets >$1M, mitigate AI-specific risks, run Scrum ceremonies, align stakeholders, and report KPIs for executive audiences.

Design, deploy, and operate secure, scalable end-to-end AI and Generative AI systems across cloud and on-prem environments. Build APIs, integrate foundation and open-source models, implement MLOps pipelines, monitoring, and FinOps, optimize inference cost, and work with vector databases, embedding retrieval, and large-scale data processing for enterprise clients (banking/financial services).

Design, build and deploy production-grade generative AI systems, RAG pipelines, and multi-agent frameworks. Train, fine-tune, and optimize NLP and CV models; integrate cloud and open-source foundation models; implement data preprocessing, vector search, and MLOps for secure, high-availability enterprise deployments.

Engineer, deploy, and scale enterprise-grade AI systems (conversational agents, RAG, automation) for banking. Build MLOps pipelines, vector DB/knowledge-graph search, APIs/CI-CD, model optimization/fine-tuning, observability, and standards for fairness, security, and resilience. Translate analytics to business value and present technical solutions to stakeholders.

Design and build responsive, accessible React UIs and reusable component libraries; implement data-heavy dashboards, manage complex state, optimize performance, write tests, and integrate REST APIs and backend services (Node.js/Python). Collaborate with UX, backend, and AI/ML teams and contribute to architectural and delivery decisions.

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.

Lead design and delivery of enterprise-scale data architectures and models supporting treasury, analytics, and AI pipelines. Define governance, security, and integration strategies across banking systems and cloud/on-prem platforms. Collaborate with engineers, data scientists, and stakeholders to optimize performance, ensure regulatory compliance, and document data assets while evaluating emerging data technologies.

The Senior Cloud Solutions Architect will design secure, scalable cloud-native banking platforms on Azure and AWS, ensuring compliance with banking regulations while guiding engineering teams in implementation.