Top Tech Jobs & Startup Jobs

Manage the architecture, installation, configuration, upgrades, security, integrations, and ongoing operation of enterprise PeopleSoft environments. Administer PeopleTools, WebLogic, Tuxedo, application and process scheduler servers, search platforms, Oracle databases, and Linux/Unix systems. Support high availability and disaster recovery, cloud and middleware integrations, automation, troubleshooting, documentation, code and design reviews, and mentorship.

Operate and improve the reliability, availability, performance, and operational excellence of large-scale distributed systems. Build automation and tooling, manage Linux and Kubernetes environments, design CI/CD pipelines, implement observability, troubleshoot production systems, and reduce operational toil. Preferred work includes defining SLOs and error budgets, chaos engineering, cloud operations, capacity planning, performance engineering, load testing, and service mesh implementation.

Designs, evaluates, and deploys advanced machine learning systems from research prototypes through scalable production pipelines. Responsibilities include training and fine-tuning deep learning models, applying current AI research, implementing distributed and mixed-precision training, building observability and safeguards, and continuously improving deployed systems. The role also involves evaluating emerging techniques, communicating complex methods, and delivering impactful applied AI projects.

Designs, builds, and optimizes SAP HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA platforms. Translates business requirements into performant models using calculation views, CDS views, AMDPs, and SQLScript. Diagnoses HANA performance issues, applies tuning methodologies, supports replication and security models, and contributes to analytics, migrations, and upgrades. The role requires strong HANA architecture, SQL, troubleshooting, communication, and documentation skills.

Architect, deploy, and operate secure, resilient Oracle Cloud Infrastructure environments. Own landing-zone design, infrastructure-as-code, workload migration, ongoing operations, cost optimization, observability, automation, and incident response. Partner with database, application, and security teams to support mission-critical Oracle and non-Oracle workloads.

Design, build, and operate scalable AI inference and model-serving platforms for production workloads. Responsibilities include optimizing LLM inference, GPU utilization, latency, throughput, autoscaling, routing, and distributed workloads. The role also develops deployment, monitoring, observability, security, high-availability, MLOps, cloud optimization, and FinOps solutions while mentoring teams and evaluating emerging AI infrastructure technologies.

Lead the enterprise architecture, governance, security, integrations, and long-term evolution of the ServiceNow platform across ITSM, ITOM, HRSD, CSM, SecOps, GRC, and custom applications. Define CMDB, Discovery, Service Mapping, workflow, data quality, scalability, and release standards. Oversee integrations, platform performance, upgrades, and security while mentoring developers and administrators and partnering with business and technical stakeholders.

Designs, implements, and operates security controls across Oracle Cloud Infrastructure, Oracle Cloud Applications, and hybrid Oracle environments. Responsibilities include cloud security architecture, workload hardening, identity and access management, vulnerability and compliance programs, incident response, threat detection, SIEM integration, and automation. The engineer collaborates with cross-functional teams, reviews designs and code, documents solutions, and mentors junior engineers.

Build and operate large-scale data infrastructure for AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, and high-throughput delivery across diverse modalities. The role requires operating petabyte-scale storage and pipeline systems, supporting machine learning workloads, and applying strong software engineering fundamentals to improve model quality and training efficiency.

Design, build, and operate reliable, high-performance production inference platforms for large machine learning models. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, performance engineering, capacity planning, and incident response. The role requires distributed systems expertise, experience with LLM inference frameworks, Kubernetes, cloud platforms, and high-throughput services. Preferred experience includes open-source contributions, globally distributed AI serving, model optimization, FinOps, and external-facing AI APIs.