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Designs, builds, and maintains production data pipelines and infrastructure supporting analytics, machine learning, and financial decision-making. Responsibilities include data ingestion, modeling, quality validation, workflow orchestration, database optimization, monitoring, troubleshooting, and managing scalable AWS or on-premises systems. The role collaborates with data scientists and analysts to ensure reliable access to large-scale and time-series financial data.

Evaluate and validate machine learning outputs against financial theory, client objectives, and market dynamics. Translate research signals into investment strategies, portfolio construction, and testable modeling specifications. Design and scale quantitative models in Python and Java, develop financial validation frameworks, and collaborate with ML researchers and technical teams to integrate research into production. Communicate quantitative insights clearly across technical and business stakeholders.

Own the full new-business sales cycle for MDOTM’s AI-powered SaaS investment platform, Sphere. Prospect and build pipelines, engage institutional asset and wealth management clients, deliver tailored demonstrations, manage complex multi-stakeholder evaluations, negotiate commercial terms, and close high-value enterprise deals. The role requires a proven B2B sales hunter who can independently generate net-new logos, exceed targets, influence senior stakeholders, and sell emerging technology within financial services.

Develop and optimize scalable Java and Spring Boot backend services. Build clean, testable code; implement relational and NoSQL database solutions; apply software architecture patterns; improve performance, security, CI/CD, and deployments; and collaborate with DevOps teams and stakeholders. The role may also involve messaging systems, cloud infrastructure, APIs, Docker, and Kubernetes.

Manage end-to-end B2B client relationships, lead onboarding and training, monitor engagement and performance, define and track success metrics, deliver tailored presentations, act as client advocate to drive product improvements, and identify opportunities for account growth and improved product adoption.

Support and enhance platform infrastructure and developer tooling: build and automate GitLab CI/CD pipelines, manage IaC (Terraform, Ansible), containerize services (Docker, Kubernetes), implement monitoring/logging (Prometheus, Grafana, ELK, Zabbix), automate database operations (MySQL, MongoDB), embed security best practices, participate in on-call rotations, document runbooks, and promote DevOps culture across product teams.

Lead product development for Sphere, defining vision and roadmap, prioritizing features, working with engineering, research, and design, applying agile practices, and ensuring strong user experiences and stakeholder communication for an AI-driven investment platform.

The Backend Engineer develops and optimizes a Spring Boot application, implements best practices, troubleshoots performance, and collaborates with DevOps teams.

Lead design, development, and optimization of scalable Spring Boot microservices. Drive architectural decisions (Layered Architecture, DDD, Event-Driven), mentor engineers, collaborate with Product and DevOps, troubleshoot performance and security, and improve CI/CD and deployment automation. Work with relational and NoSQL databases and messaging systems.

As a Product Designer at MDOTM, you'll create user-friendly digital experiences, lead design from ideation to production, and advocate for users' needs while collaborating with cross-functional teams.