Chief Machine Learning Infrastructure Architect
5 days ago
A Senior MLOps Engineer is sought after to lead in the development, implementation, and scaling of AI infrastructure.
- Responsibilities:
- Design and implement scalable ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models. These pipelines should ensure high efficiency and reliability.
- Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows. This includes ensuring seamless integration with existing systems.
- Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization. The chosen solution must meet these performance requirements.
- Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices. Implementing a robust data management system is crucial.
- Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks. Ensure that integrated solutions scale efficiently.
- Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations. The architecture must prioritize scalability and security.
- Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads. This will facilitate efficient monitoring and maintenance of the system.
- Maintain CI/CD pipelines for Python (FastAPI) and TypeScript (NestJS) inference services. Ensure smooth transitions between development and production environments.
- Define and track SLAs/SLOs, optimize cloud spend by ≥ 20% year-over-year, and ensure GPU clusters operate at > 80% utilization. Effectively managing resources is key to success.
- Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals. Foster collaboration across departments to achieve shared objectives.
- Mentor junior engineers, run quarterly brown-bags, own onboarding docs (upskill 5+ engineers/quarter), and publish ≥ 1 open-source contribution or talk annually. Promote knowledge sharing and growth within the team.
- 9-14 years in software engineering, including ≥ 4 years in MLOps or ML infrastructure.
- Strong expertise in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow.
- Experience with inference frameworks (Triton, TensorFlow Serving, BentoML, TorchServe).
- Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration.
- Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.).
- Proven track record building reliable, scalable ML systems in production.
- Vector DB integration (Qdrant, Pinecone).
- Agent orchestration (LangChain, LlamaIndex).
- Multi-tenant security and RBAC.
- Observability stacks (Prometheus/Grafana, ELK).
- CI/CD for FastAPI/NestJS services.
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