Quick overview
Job description
What You’ll Get To Do
- Design and manage scalable ML infrastructure with IaC tools (Terraform, CloudFormation).
- Develop and optimize cloud-based pipelines for training, evaluation, and inference on multimodal datasets.
- Build and operate data systems for large-scale video ingestion, indexing, and storage.
- Maintain MLOps workflows for versioning, experiment tracking, reproducibility, and CI/CD.
- Ensure reliability and observability with monitoring, logging, and alerting.
- Collaborate with AI/ML Engineers to productionize workflows.
- Optimize infrastructure for performance and cost across cloud and edge.
- Enforce best practices in security, compliance, and maintainability.
- Mentor and manage junior engineers, providing technical guidance and career development.
What You Have
- Bachelor’s/Master’s in Computer Science, Engineering, or related field (or equivalent experience).
- 4+ years of industry experience in ML infrastructure or platform engineering.
- Strong coding skills in Python/TypeScript and a strong foundation in software engineering best practices.
- Proven experience with distributed systems, cloud platforms (AWS preferred), containerization and orchestration (Docker, Kubernetes/EKS, Ray), and serverless.
- Hands-on experience building ML pipelines for distributed training and large-scale inference.
- Strong knowledge of data management at scale, including preprocessing and retrieval of video/image datasets.
- Proficiency with CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and automation.
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow).
- Experience with system monitoring and observability in production.
The Extras That Set You Apart
- Experience with vector databases (OpenSearch, Pinecone, Weaviate) for indexing and retrieval.
- Familiarity with distributed training frameworks (Horovod, DDP/FSDP, DeepSpeed, Ray).
- Hands-on experience with GPU orchestration and auto-scaling (Karpenter, SageMaker, EKS).
- Experience with agentic AI deployment workflows, orchestration frameworks, and retrieval-augmented generation.
- Strong knowledge of security and compliance in ML and cloud environments.
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