Software Gigs

Staff Software Engineer role designing and implementing cloud-native platform components and services, with primary focus on Kubernetes workloads, distributed systems, and infrastructure automation. Requires 5+ years hands-on Kubernetes experience, proficiency in Go or systems languages, and demonstrated expertise building operators, controllers, and platform services. Will lead well-scoped projects from design through production, contribute to reliability and scalability, mentor junior engineers, and participate in on-call operations.

Sr. Director role overseeing SIE's data and AI platform ecosystem, including data infrastructure (streaming, ingestion, warehouse), experimentation platforms, and governance tools. Responsible for defining long-term vision, leading global engineering teams, and ensuring platforms process massive data volumes to deliver insights across gaming, content creation, and enterprise operations. Must drive scalability, cost efficiency, security, and operational excellence while collaborating with senior leadership to align platform strategy with business objectives. Requires proven Sr. Director-level experience and thought leadership in platform architecture, real-time data processing, and ML/AI integration.

Design and implement Infrastructure as Code to automate provisioning, monitoring, and lifecycle management of NoSQL, Streaming, and Caching platforms (Cassandra, Aerospike, Kafka, Redis) across AWS and GCP. Build highly available, self-healing systems with automated failover and scaling, develop comprehensive observability solutions, and lead incident response for critical data platform issues. Drive automation-first practices and apply AI/ML approaches such as anomaly detection and predictive scaling to enhance reliability and reduce manual toil. Partner with engineering and platform teams to ensure resilient infrastructure supporting billions of transactions and millions of players globally.

Design and architect next-generation ML inference infrastructure for globally distributed, multi-tenant model serving with high availability, scalability, and cost efficiency. Lead the development of low-latency, high-throughput inference systems supporting computer vision and multimodal models (CNNs, segmentation, object detection) using Scala, Java, and Go. Build large-scale distributed systems with reactive frameworks, integrate enterprise feature stores, and extend CI/CD pipelines (GitHub Prow, Pulumi) with automation and policy enforcement. Design advanced observability frameworks, optimize ML algorithms for performance, mentor engineers, and ensure MLOps and compliance standards (GDPR, SOC2) across the platform.

The AI Architect will lead the design, development, and deployment of enterprise-scale generative AI solutions across Azure and AWS, partnering with business and engineering leaders to define AI strategy and architecture standards. The role requires deep expertise in modern LLMs (Claude, Gemini, OpenAI), multi-cloud AI platforms, and enterprise architecture, with 10+ years in software/cloud architecture and 5+ years architecting AI/ML solutions. Key responsibilities include designing end-to-end GenAI solutions leveraging RAG, agentic AI, and multi-agent systems; establishing cloud-native deployment patterns; and defining governance, compliance, and responsible AI frameworks. The ideal candidate will serve as a trusted technical advisor to executives, mentor engineering teams, and drive AI adoption through reusable frameworks and reference architectures.

As a Staff Software Engineer in Data Platform Engineering, you will design, build, and operate cloud-native platform components and services, taking features from design through production. You'll lead well-scoped technical projects, collaborate with senior engineers on architecture alignment, and ensure reliability and scalability through testing and instrumentation. You'll spend most of your time hands-on building operators, controllers, automation, and platform services that other teams depend on, while mentoring junior engineers. The role requires 8+ years of software development experience (or equivalent with advanced degrees), 5+ years of hands-on Kubernetes experience, and proficiency with at least one major hyperscaler (AWS, Azure, GCP).

Design, implement, and maintain secure DevSecOps automation frameworks across on-premise and cloud infrastructure, integrating security controls into CI/CD pipelines for application and configuration deployment. Develop Infrastructure as Code using Terraform, Ansible, ARM/Bicep, PowerShell, Python, and Bash across Windows, Linux, VMware, Azure, and hybrid environments. Automate provisioning, patching, vulnerability remediation, and compliance checks aligned with FFIEC, NCUA, CIS, and NIST standards while partnering with Cybersecurity, Infrastructure, and Application Development teams. Support container/platform engineering, cloud identity governance, and promote DevSecOps and GitOps practices across technology teams.

Own the complete release and deployment strategy for Elemynt's secure AI infrastructure platform, building systems that ensure every release is versioned, reproducible, observable, and safe to operate across enterprise and scientific computing environments. Design and implement deployment automation, CI/CD pipelines, artifact management, and operational visibility (logs, metrics, alerts) that work reliably in customer-managed and restricted environments. Debug incidents, identify root causes, and systematize fixes into reusable automation. Partner with product and engineering teams to embed deployment readiness into the platform from the start.

Design and own cloud infrastructure foundations, core platform services, and observability systems that enable reliable deployment across customer environments (on-premise, cloud, or hybrid). Build production-grade Kubernetes infrastructure, internal APIs, deployment pipelines, and monitoring/alerting layers using infrastructure-as-code. Instrument service-level objectives and health signals to ensure measurable reliability and reproducible, secure deployments across all environments.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.

Ignite Digital seeks an AI Engineer to own the end-to-end machine learning lifecycle—from data preparation and model training through production deployment and monitoring. You will integrate AI models into frontend and backend systems, optimize performance for latency and cost, and collaborate with technical leads and customers to deliver Data and AI solutions. The role requires strong Python and cloud platform expertise (AWS, Azure, or GCP), software engineering fundamentals, and the ability to communicate technical opportunities and limitations across varying levels of technical experience.

Lead operational service delivery for enterprise Web Application Firewall (WAF), load balancing, DNS, certificates, and edge security platforms. Own end-to-end service performance, SLA/KPI metrics, incident escalation, and cross-team coordination across application, cloud, networking, and security teams. Manage and develop senior and staff-level engineers, drive operational governance, risk management, and continuous improvement. The role requires sufficient technical depth to guide platform designs, direct incident response, and make informed operational decisions, though not necessarily performing all configurations personally.

Lead an embedded Linux software team building mission-critical defense systems, splitting time between management and individual contribution. Design and implement C/C++ Linux daemons, REST APIs, and system services for aerial platforms and deployable manufacturing infrastructure. Manage team standups, architecture reviews, and cross-functional coordination with autonomy, avionics, and mechanical/electrical engineering. Engineer for real-time performance, security, and reliability while owning developer experience, CI/CD pipelines, and field operability.

Lead the architectural design of server-side, backend, and cloud platform capabilities supporting Dexcom's continuous glucose monitoring products. Collaborate across product, cybersecurity, firmware, data, and mobile teams to translate business and regulatory requirements into secure, scalable, and maintainable platform solutions. Establish architectural direction for distributed services, APIs, data pipelines, event-driven systems, and shared platform capabilities. Guide technology exploration, system integration, performance testing, and serve as an authority for platform architecture principles and modern backend development practices across the organization.

Lead Veracyte's Quality Engineering Platform team as Manager of Software Test Automation, defining quality strategy and building scalable automation frameworks that enable product teams to own software quality. Manage a team of SDETs, Automation Engineers, and Quality Platform Engineers to develop self-service testing platforms, reusable automation libraries, and AI-assisted quality tools supporting unit, integration, API, UI, end-to-end, regression, performance, security, and compliance testing. Establish modern engineering practices, quality standards, and reference architectures across the organization while fostering a culture of innovation and continuous improvement. Drive Veracyte's strategy for AI throughout the software testing lifecycle, including automated test generation, regression analysis, defect classification, and synthetic test data creation.

Design, build, and operate cloud-native platform features and services with hands-on engineering focused on Kubernetes, distributed systems, and infrastructure automation. Deliver well-defined work iteratively from implementation through production, contributing to reliability, scalability, and operability through testing, instrumentation, and on-call participation. Collaborate with senior engineers on platform standards while mentoring junior team members. Requires 5+ years of software development experience (or equivalent), production experience with Kubernetes, exposure to at least one major hyperscaler (AWS, Azure, GCP), and proficiency in Go or another systems language.

Design, implement, and maintain secure DevSecOps automation frameworks and CI/CD pipelines across hybrid infrastructure, cloud, and application platforms. Develop Infrastructure as Code using Terraform, Ansible, ARM/Bicep, PowerShell, and Python to automate provisioning, configuration management, patching, and vulnerability remediation across Windows, Linux, VMware, and Azure environments. Integrate security controls into deployment pipelines including vulnerability scanning, secrets detection, code scanning, and policy enforcement aligned with FFIEC, NCUA, CIS, and NIST standards. Partner across Infrastructure, Security, and Application Development teams to advance DevSecOps practices, container engineering, and secure cloud governance.

Own and evolve infrastructure, deployment systems, and cloud environments powering Clinically AI's healthcare AI platform. Design and maintain GCP infrastructure with focus on GKE, Kubernetes, and secure multi-environment deployments; build Infrastructure-as-Code using Terraform and Helm charts; optimize CI/CD pipelines with GitHub Actions; implement observability, security best practices, and incident response. Work closely with Backend, AI, and Product teams to support scalable infrastructure for services, AI pipelines, and high-throughput workloads.

Join Singular Genomics' core software team as a generalist Software Engineer contributing to the sequencing analysis pipeline, operational tooling, and field support. You'll work across Python development, CI/CD infrastructure (Jenkins, Bitbucket), HPC job execution, and diagnostic troubleshooting of production failures on genomic sequencers. Responsibilities include pipeline bug fixes and features, build system maintenance, field triage, data handling logic, and developing lightweight web services and internal APIs. You need 2–5 years of professional experience, solid Python fundamentals, Linux/HPC familiarity, and the ability to debug and move fast across a large, real production codebase.

Own and evolve the cloud infrastructure, deployment systems, and CI/CD pipelines powering Clinically AI's healthcare platform on GCP. Design and maintain cloud infrastructure using GKE, Terraform, and Helm; build reliable CI/CD pipelines with GitHub Actions; implement observability, security best practices, and incident response processes. Work closely with Backend, AI, and Product teams to support scalable infrastructure for AI pipelines, real-time processing, and multi-environment deployments. Operate with significant autonomy across cloud-native architecture while optimizing for performance, reliability, cost, and compliance.

Staff Site Reliability Engineer at SHEIN responsible for operating and evolving large-scale, mission-critical production systems with 24/7/365 on-call participation. Design, build, and maintain observability solutions (metrics, logs, traces, alerting) with AI-powered anomaly detection; own and operate core open-source infrastructure (APISIX, Nginx, Kubernetes, Kafka, Elasticsearch, Redis, Consul, Etcd, Zookeeper). Automate operational workflows, reduce incident frequency and MTTR, and provide technical leadership across global engineering teams. Requires strong software engineering skills, deep Linux/networking/distributed systems expertise, and passion for solving problems at scale.

Lead end-to-end development of AI agents and autonomous engineering tools for Teradata's enterprise platform, managing multiple global engineering teams. Shape engineering strategy for agent platforms, LLM gateways, vector stores, observability frameworks, and low-code/no-code/pro-code agent frameworks. Drive architecture standardization, governance practices, operationalization pipelines, and ROI metrics while partnering with product, architecture, and AI platform teams. Requires 12+ years software engineering experience with 5+ years in technical leadership, hands-on expertise with LLMs, agent frameworks, distributed systems, cloud platforms (AWS, Azure, GCP), and modern CI/CD tools.