Top Tech Jobs & Startup Jobs

Designs, optimizes, and deploys machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and systems engineering. The role also addresses on-device privacy, security, and cross-functional collaboration, with preferred experience in NPU/DSP toolchains, federated learning, safety-critical deployments, open-source frameworks, and on-device LLM optimization.

Designs, builds, operates, and optimizes large-scale Hadoop data pipelines and analytics platforms. Responsibilities include ETL/ELT, streaming pipelines, Spark and MapReduce tuning, data modeling, governance, monitoring, orchestration, documentation, architecture reviews, and mentoring. The role partners with data scientists and analysts to deliver reliable datasets and evaluates cloud and lakehouse technologies.

Designs, develops, tests, deploys, monitors, and supports enterprise integrations connecting SAP S/4HANA and other SAP systems with third-party and cloud platforms. The role requires deep SAP CPI and Integration Suite expertise, iFlow development, scripting, API management, integration patterns, event-driven architectures, security protocols, and SAP PI/PO migration experience. The developer collaborates with architects, functional consultants, and integration partners to deliver secure, scalable solutions.

Designs, develops, deploys, and operates enterprise integrations using the MuleSoft Anypoint Platform. Responsibilities include API design, Mule flow development, security, observability, troubleshooting, CI/CD, and production optimization. The role partners with architects, application teams, and business stakeholders to deliver reusable integration solutions, conducts design and code reviews, and mentors junior engineers. Integrations span major SaaS platforms and may use event-driven technologies such as Anypoint MQ and Kafka.

Designs and maintains Workday configurations, advanced reports, calculated fields, business processes, security models, integrations, and Workday Extend or PRISM solutions. Partners with HR, payroll, finance, IT, and integration teams to translate requirements into maintainable platform solutions. Responsibilities include tenant management, release adoption, regression testing, troubleshooting production issues, documentation, best-practice guidance, and mentoring junior engineers.

Designs, deploys, automates, and operates enterprise VMware virtualization, networking, storage, and hybrid-cloud environments. Responsibilities include managing vSphere, vSAN, NSX, Tanzu, Aria, VCF, disaster recovery, capacity, observability, upgrades, security patching, and cloud integrations. The engineer troubleshoots complex infrastructure issues across compute, network, and storage layers and develops automation using PowerCLI, Terraform, Ansible, and vRO.

Build and operate high-throughput backend platforms and shared services, including request routing, traffic management, caching, asynchronous jobs, and event processing. Design distributed systems for high availability, consistency, and predictable latency. Lead multi-quarter platform initiatives, make technical decisions, mentor engineers, improve developer experience, and collaborate across engineering teams through design reviews, documentation, and stakeholder management.

Architect, deploy, secure, and operate large-scale Apache Kafka and Confluent environments across cloud and on-premises infrastructure. Responsibilities include cluster design, Kafka Connect pipelines, HA/DR, CI/CD automation, observability, capacity optimization, incident response, developer enablement, documentation, and mentoring. The engineer will partner with application teams and stakeholders to deliver reliable event-streaming platforms and evaluate emerging technologies.

Designs, builds, and operates reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, capacity planning, and performance optimization. The role requires distributed systems expertise, experience with LLM inference frameworks, cloud and Kubernetes familiarity, and the ability to balance latency, throughput, cost, and model quality while supporting large-scale AI services and incident response.

Designs and governs enterprise Salesforce implementations across multiple clouds and orgs. Establishes technical strategy, security, integration, customization, data, and platform standards while partnering with stakeholders and engineering teams. Leads complex Salesforce programs end-to-end and delivers scalable solutions using Apex, Lightning Web Components, Flow, Salesforce DX, and modern DevOps practices.