Top Tech Jobs & Startup Jobs

Design, build, and maintain Terraform-based infrastructure-as-code for cloud and hybrid environments. Develop reusable modules, manage state and workspaces, harden CI/CD pipelines, enforce policy-as-code, and standardize infrastructure delivery across cloud providers and teams. Troubleshoot complex infrastructure issues, apply strong Git workflows, and support secure, scalable management of large resource environments. Preferred experience includes multi-cloud Terraform, orchestration tools, policy engines, public module contributions, and FinOps cost governance.

Design and operate large language model fine-tuning workflows using supervised learning, DPO, RLHF, LoRA, and QLoRA. Build distributed GPU training pipelines, curate high-quality datasets, optimize training performance, and establish rigorous automated and human evaluation suites. Monitor safety and policy behaviors, manage reproducible model artifacts, troubleshoot large-scale training failures, collaborate with cross-functional teams, document technical decisions, and mentor engineers.

Designs, builds, and operates production ML inference platforms for LLMs, vision, and recommendation models. Responsibilities include optimizing batching, routing, caching, autoscaling, GPU utilization, observability, security, deployment workflows, incident response, and reliability. The engineer partners with ML and product teams to launch models, improve serving performance, manage capacity and costs, and apply emerging AI serving techniques.

Define and lead enterprise data-platform architecture across ingestion, storage, processing, governance, and consumption. Establish standards for data modeling, lifecycle management, cloud infrastructure, security, networking, identity, and cost optimization. Partner with engineering, analytics, ML, and business teams while leading complex, cross-functional data-platform initiatives. The role requires expertise in modern data platforms, lakehouse architecture, streaming, governance, lineage, catalogs, and production-scale data processing.

Designs and governs cloud-native architectures across compute, networking, storage, identity, data, security, and observability. Leads architecture reviews, landing-zone and connectivity strategy, migrations, modernization, cost optimization, compliance, and reference architecture development. Partners with stakeholders, security teams, engineering organizations, finance, and cloud providers while mentoring technical teams and evaluating emerging cloud capabilities.

Designs and operates large-scale observability platforms covering metrics, logs, traces, dashboards, storage, and alerting. The role requires expertise with Prometheus, Grafana, commercial observability tools, OpenTelemetry, distributed tracing, structured logging, high-throughput pipelines, SRE practices, Linux, networking, containers, CI/CD, and incident management. Preferred experience includes Thanos, Mimir, Cortex, Loki, Tempo, eBPF, open-source contributions, cost optimization, and regulated environments.

Designs, builds, and operates secure, highly available AWS cloud platforms and production workloads. Responsibilities include cloud architecture, infrastructure as code, automation, security, observability, CI/CD, cluster operations, troubleshooting, cost optimization, and continuous operational improvement. The engineer partners with application, security, and SRE teams to deliver scalable cloud-native solutions.

Define organization-wide standards for LLM applications, design prompts and agentic workflows, lead retrieval-augmented generation pipelines, and build evaluation frameworks and developer tooling. Establish safety guardrails, conduct red-teaming, optimize latency, cost, and quality, review architectures, mentor engineers, and guide responsible adoption of LLM capabilities across product teams.

Designs, deploys, and operates Microsoft Azure cloud infrastructure and AKS clusters at scale. Builds infrastructure as code, CI/CD pipelines, automation, monitoring, alerting, and observability for distributed workloads. Manages cloud security, identity, compliance, troubleshooting, documentation, and hybrid or multi-cloud environments. The role also supports cloud cost management and regulated workloads.

Design, evaluate, and productionize applied AI and machine learning systems across language, vision, recommendation, and structured data. Responsibilities include research experimentation, deep learning model training and optimization, production pipeline development, data quality tooling, responsible AI evaluation, model monitoring, technical documentation, mentoring, and influencing the AI roadmap.