Jobs at Deeplight AI

Own project controls, portfolio governance, delivery health analysis, RAID tracking, executive dashboards, KPI reporting, stage-gate compliance, and audit-ready documentation across enterprise AI and data projects. The role also improves PMO standards, reporting workflows, and project management practices while collaborating with project managers, technical leads, and senior stakeholders.

Leads enterprise data architecture for fixed-scope AI consultancy projects. Designs and governs Databricks Lakehouse platforms across AWS and Azure, advises executive clients, manages architecture roadmaps and technical risk, mentors cross-functional engineering and data teams, and establishes secure, compliant data foundations. The role requires strong consulting, stakeholder management, cloud, distributed processing, MLOps, and regulated financial-services experience.

Design and build scalable AWS Lakehouse solutions using Databricks, Delta Lake, PySpark, Kafka, and SQL. Develop batch and real-time pipelines for complex financial data, optimize cloud performance and costs, and implement banking-grade security, governance, encryption, and compliance. Support machine learning and MLOps workflows through feature engineering, MLflow, Docker, EKS, and SageMaker. Automate infrastructure and delivery with Terraform and CI/CD while communicating technical solutions to stakeholders.

Develop statistical risk models and quantitative solutions for corporate lending, wholesale banking, treasury, liquidity, and regulatory capital. Build PD, LGD, EAD, RAROC, and RAC frameworks; apply Basel requirements and stress testing standards; write production-quality Python; and communicate model methodologies to banking clients and stakeholders. Preferred experience includes causal analysis, sequence models, large-scale distributed computing, and executive-facing dashboards.

Lead end-to-end data engineering delivery for client engagements by architecting and deploying production-grade pipelines, lakehouses, warehouses, and analytics datasets. Establish engineering standards for testing, CI/CD, governance, security, monitoring, and documentation. Resolve complex technical issues, conduct code reviews, mentor engineers, and advise client stakeholders on scalable data platforms supporting analytics, machine learning, and generative AI.

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.

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).

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.

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.