Senior AI Platform Engineer
CyberCoders
Job Description
Job Description
Senior AI Platform Engineer
Senior AI Platform Engineer
Location: San Francisco, CA - Hybrid 3 days a week
(Near Oracle Park)
Position OverviewWe are seeking a Senior AI Platform Engineer to design, build, and operate a scalable, secure platform that enables ML engineers and data scientists to develop, deploy, and monitor large-scale AI/LLM applications. The role focuses on infrastructure for model training and inference, integration with LLM APIs, vector databases and orchestration of AI workflows across cloud environments with infrastructure-as-code and strong automation. Key Responsibilities
- Design, build, and maintain scalable AI/ML infrastructure for training, evaluation, and production inference across cloud providers.
- Implement and operate model serving and inference platforms integrating LLM APIs (OpenAI, Anthropic, etc.) and self-hosted models.
- Develop and maintain vector database integrations (e.g., Pinecone, Milvus, FAISS) for semantic search and retrieval-augmented generation.
- Author and maintain Infrastructure-as-Code (Terraform, CloudFormation, or equivalent) and CI/CD pipelines to automate environment provisioning and deployments.
- Build and maintain containerized workloads and orchestration on Kubernetes (or managed K8s services) for training and inference.
- Implement AI orchestration and workflow tools (e.g., Airflow, Dagster, Prefect, Kubeflow, Ray) to schedule and manage end-to-end ML pipelines.
- Establish observability, logging, metrics, and alerting for model performance, data drift, latency, and cost using tools like Prometheus, Grafana, and application tracing.
- Collaborate closely with ML engineers, data scientists, security and SRE teams to ensure reproducible experiments, secure deployments, and cost-effective operations.
- Optimize system performance and cost at scale, including autoscaling, batching, sharding, and caching strategies for low-latency inference.
- Mentor junior engineers, own architecture and operational best practices, and contribute to technical roadmap and cross-functional planning.
Qualifications
- 5+ years of engineering experience building cloud-native infrastructure for production systems; 3+ years focused on AI/ML platforms or MLOps.
- Strong proficiency in Python for automation, tooling, and integrations with ML frameworks and APIs.
- Hands-on experience integrating and operating LLM APIs and model serving frameworks for inference at scale.
- Deep experience with cloud infrastructure (AWS, GCP, or Azure) including networking, IAM, managed services, and cost controls.
- Proficiency with Infrastructure-as-Code tools (Terraform, CloudFormation, Pulumi) and experience designing reproducible provisioning processes.
- Experience with Kubernetes, Docker, and container orchestration for machine learning workloads.
- Practical knowledge of vector databases and embedding pipelines (e.g., Pinecone, Milvus, FAISS) and retrieval-augmented generation patterns.
- Experience with AI orchestration and workflow tools (e.g., Airflow, Dagster, Prefect, Kubeflow, Ray) to build repeatable ML pipelines.
- Strong observability and SRE practices for ML systems: monitoring, logging, tracing, and incident response.
- Solid engineering fundamentals: distributed systems, networking, security, testing, and CI/CD best practices.
- Excellent communication and collaboration skills; experience mentoring engineers and driving cross-team initiatives.
- Bachelors degree in Computer Science, Engineering, or equivalent experience; advanced degree or relevant certifications preferred.
Vacancy posted 1 day ago
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