About the Role:
As an MLOps Engineer, you set the ML infrastructure and reliability strategy across the platform, focusing on the deployment, fine-tuning, and serving of LLMs and other deep learning models at scale. You will be responsible for architecture decisions within both SaaS and on-prem operating models, mentor engineers across teams, and drive a multi-quarter ML infrastructure strategy, functioning as a force multiplier.
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What You'll Do:
- Define ML infrastructure architecture across the platform, including model deployment strategy (vLLM, Triton, or TGI), pipeline engineering (MLflow or Kubeflow), and cloud-native infrastructure across major cloud platforms (AWS, Azure, or GCP).
- Set direction for ML system reliability, including monitoring, latency/throughput/availability targets, and incident response across research and production environments.
- Mentor senior MLOps engineers; raise the operational bar across multiple teams.
- Drive cross-team initiatives to improve inference performance and cost-efficiency, including distributed training frameworks (DeepSpeed, FSDP, Accelerate).
- Partner with ML researchers, product, and engineering leadership on multi-quarter ML infrastructure strategy.
- Ensure ML infrastructure scales across managed SaaS and fully air-gapped on-prem deployments.
What You'll Bring:
- 10+ years of MLOps, ML infrastructure, or machine learning engineering with a history of architectural ownership.
- Proven track record in architecting large-scale model deployment (including LLMs) and ML infrastructure at scale.
- Deep cloud expertise across major cloud platforms (AWS, Azure, or GCP) and strong proficiency in Python.
- A mentorship record where engineers you have grown now operate independently at higher levels.
- Deep comfort architecting ML systems that run in both managed SaaS and on-premises/disconnected air-gapped environments.
- Architectural depth in Kubernetes, including GPU scheduling, multi-tenancy, operators, and understanding of failure modes of distributed workloads on shared clusters.
- Strong communication, stakeholder management, and decision-making skills, with a passion for building diverse, inclusive engineering teams.
Strong Preference:
- Experience owning production reliability at the platform level, including SLO definition, incident command, postmortem practice, and driving reliability improvements across teams rather than services.
- Architectural experience with distributed training and fine-tuning at scale (DeepSpeed, FSDP, Megatron-LM), including cluster design, checkpointing strategy, and failure recovery.
- Deep knowledge of GPU systems, including CUDA, NCCL, interconnect topology (NVLink, InfiniBand/RoCE), and diagnosing performance and communication problems across nodes.
- Model optimization strategy at the portfolio level, including quantization (FP8, AWQ, GPTQ) and speculative decoding with measurable cost or latency outcomes across multiple systems.
- Experience in regulated or security-constrained environments with compliance-driven architecture, model governance, lineage, audit, and secrets management.
- Experience in on-prem/air-gap ML delivery architecture at scale.
- A track record of maturing MLOps practice in a growing organization with standards, platform abstractions, and paved paths that continue beyond your involvement.
- Experience with bare-metal GPU cluster architecture, including scheduling (Slurm or Kubernetes) and hardware lifecycle in customer or owned data centers.
Nice to Have:
- Experience with conference speaking, technical writing, or industry thought leadership.
- Contributions to open-source projects related to inference, serving, or ML infrastructure, particularly at a maintainer level.
- C/C++ or CUDA kernel experience for performance-critical paths.
- Proficiency in Arabic language.
Why Join Us:
- Mission-Driven Work: Engage on cutting-edge AI applications with a dedicated team, tackling real-world challenges in critical sectors.
- Unparalleled Opportunity: Seize the chance to innovate and address real-world challenges using AI at a company with unique access to world-leading models and resources.
- Career Growth: Benefit from competitive compensation, benefits, and significant career advancement opportunities as a foundational team member.
- World-Class Environment: Experience a flexible working environment equipped with the latest tools and technologies necessary for your success.
