Title: ML Engineer (Local to Arkansas)

Duration: Contract

Location: Bentonville, Arkansas (Onsite)

Job Description:

Key Responsibilities:

Work with client's AI/ML Platform Enablement team within the eCommerce Analytics team. The broader team is currently on a transformation path, and this role will be instrumental in enabling the broader team's vision
Work closely with data scientists to help with production models and maintain them in production
Deploy and configure Kubernetes components for production cluster, including API Gateway, Ingress, Model Serving, Logging, Monitoring, Cron Jobs, etc. Improve the model deployment process for MLE for faster builds and simplified workflows
Be a technical leader on various projects across platforms and a hands-on contributor of the entire platform's architecture
System administration, security compliance, and internal tech audits
Responsible for leading operational excellence initiatives in the AI/ML space which includes efficient use of resources, identifying optimization opportunities, forecasting capacity, etc
Design and implement different flavors of architecture to deliver better system performance and resiliency
Develop capability requirements and transition plan for the next generation of AI/ML enablement technology, tools, and processes to enable client to efficiently improve performance with scale

Tools/Skills (hands-on experience is must):

Administering Kubernetes. Ability to create, maintain, scale, and debug production Kubernetes clusters as a Kubernetes administrator and In-depth knowledge of Docker
Ability to transform designs ground up and lead innovation in system design
Deep understanding of data center architectures, networking, storage solutions, and scale system performance
Have worked on at least one Kubernetes cloud offering (EKS/GKE/AKS) or on-prem Kubernetes (native Kubernetes, Gravity, MetalK8s)
Programming experience in Python, Node, Golang, or bash
Ability to use observability tools (Splunk, Prometheus, and Grafana ) to look at logs and metrics to diagnose issues within the system
Experience with Seldon core, MLFlow, Istio, Jaeger, Ambassador, Triton, PyTorch, Tensorflow/TFserving is a plus
Experience with distributed computing and deep learning technologies such as Apache MXNet, CUDA, cuDNN, TensorRT
Experience hardening a production-level Kubernetes environment (memory/CPU/GPU limits, node taints, annotations/labels, etc.)
Experience with Kubernetes cluster networking and Linux host networking
Experience scaling infrastructure to support high-throughput data-intensive applications
Background with automation and monitoring platforms, MLOps ,and configuration management platforms

Education & Experience: -

5+ years relevant experience in roles with responsibility over data platforms and data operations dealing with large volumes of data in cloud based distributed computing environments
Graduate degree preferred in a quantitative discipline (e.g., computer engineering, computer science, economics, math, operations research)
Proven ability to solve enterprise level data operations problems at scale which require cross-functional collaboration for solution development, implementation, and adoption

Inabia Solutions and Consulting, Inc.

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ML Engineer (Local to Arkansas)