Top Tech Jobs & Startup Jobs

Build and operate production MLOps systems, including model registries, experiment tracking, deployment workflows, monitoring, drift detection, evaluation gates, and rollback processes. Develop CI/CD pipelines for AI and data workloads, orchestrate Databricks training and inference jobs, implement observability and security controls, provision resources with infrastructure as code, and support incident response. Collaborate across engineering, data, and AI teams to make technical design decisions and maintain reliable, governed model services.

Leads technical solution design, customer onboarding, implementation, and ongoing support for Open Finance and digital payment solutions. Coordinates commercial, product, and engineering teams, ensures compliance with standards and governance, creates technical documentation, manages stakeholders, and mentors junior team members. The role requires strong Java development, web technology knowledge, API and SDK integration experience, customer implementation leadership, and the ability to bridge business and technical discussions.

Analyze Account Status Inquiry usage, transaction trends, customer behavior, and regional adoption to identify growth opportunities and product improvements. Build KPI reporting, translate data into product requirements and business cases, support customer and sales inquiries, and collaborate on launches, reporting, documentation, operational readiness, and customer enablement.

Leads technical solution design, customer onboarding, implementation, and ongoing support for digital payments solutions. Coordinates commercial, product, and engineering teams, ensures governance compliance, develops technical documentation and repeatable processes, manages stakeholders, and mentors junior staff. The role requires strong Java development, web technology knowledge, API and SDK integration experience, solution architecture skills, and the ability to bridge business and technical discussions.

Build platform tooling, services, and reusable patterns to apply generative and agentic AI in production. Design AI-enabled applications, evaluate models and frameworks, improve developer workflows, and deliver prototypes to production while partnering with product and engineering stakeholders.

Designs, builds, tests, and maintains scalable full-stack analytics applications, responsive user interfaces, data models, pipelines, APIs, and distributed systems. Partners with product managers and designers to deliver customer-focused features, improves engineering processes, reviews code, and mentors junior engineers. The role requires collaboration across teams and geographies while supporting analytics products for credit risk, portfolio optimization, and advertising insights.

Build and operate MLOps pipelines, model registries, deployment workflows, monitoring, governance, and production-readiness practices. Manage Databricks training and inference workloads, safe model rollouts, observability, CI/CD, security controls, infrastructure as code, containerized model services, and incident response. Collaborate across engineering, data, and AI teams while making technical design decisions and supporting reliable, governed AI/ML services.

Build and operate reliable AI/ML production systems, including model registries, experiment tracking, deployment workflows, drift monitoring, observability, CI/CD, and safe rollout mechanisms. Orchestrate training and inference workloads in Databricks, implement security and infrastructure-as-code practices, manage cloud-based resources, support incident response, and collaborate across engineering, data, and AI teams.

Build and scale Agentic AI tooling, services, and developer-facing capabilities. Design AI-enabled applications and reusable integrations, evaluate models and frameworks, improve developer experience, and deliver prototypes to production in collaboration with product and business stakeholders.

Design and build Agentic AI tooling, services, and reusable patterns; implement AI-enabled applications and developer-facing capabilities; evaluate models and frameworks; improve developer experience via automation; partner across product and engineering to prototype and scale AI solutions.