Consultant; ClearML Platform Engineer SME​/Remote

Role Name:
Consultant (Clear

ML Platform Engineer SME)

Location:

Onsite or Remote-Based Delivery with ability to travel

Duration:
Project Duration or potentially six-to-twelve (6-12) months based on demand pipeline

Skill

Summary:

Clear

ML Platform Engineer SME

1 Opportunity Overview

As a consultant aligned to Dell's Consulting Services Delivery organization, you will support customer-facing implementation engagements focused on deploying, configuring, and validating Clear

ML on the Dell AI Factory.

You will work as part of the delivery team to:

· Deploy an onsite Clear

ML platform on Dell AI Factory, proving end-to-end functionality with a single tenant.

· Conduct workshops to identify implementation requirements and validate the customer use case for Clear

ML.

· Install and configure the Clear

ML Infrastructure Control Plane for one (1) GPU cluster.

· Enable the AI Development Center.

· Enable the GenAI App Engine for endpoint launch and monitoring.

· Support functional validation activities, such as deploying one (1) sample LLM or RAG endpoint with preconfigured auth/networking via App Engine.

· Perform a basic training pipeline using the AI Development Center.

· Implement baseline RBAC for a single tenant.

2 Essential Requirements and Skills

· 3+ years of experience implementing and supporting infrastructure deployment, setup and configuration through automation.

· 1+ years of experience configuring, managing, and administering Clear

ML Platform.

· Experience in Large Language Models and Generative AI solutions, with a focus on AI development agents.

· Ability to collaborate with Customer SMEs to lead the requirement gathering, use case definition, prioritization, and model identification.

· Ability to translate business specifications to documentation.

· Understanding of data related concepts and technologies, including data models, metadata, data dictionaries, and sequences.

· Experience configuring SSO, DNS, and LDAP authentication

· Work with data science projects, workbenches, configure data connections, Deploy trained models using model serving, Create data science pipelines

· Understanding of data warehousing concepts and technologies.

· Experience with data management including data profiling, sourcing, quality, and transformation.

3 Desired and Skills

· Containers

o Kubernetes, Docker, Tanzu, Rancher

· CI/CD Pipelines and Orchestration

o Jenkins, Concourse, Service Now, Gitlab CI/CD, Tekton

· AIOps and MLOps Platforms

o Cnvrg.ai, Nemo, Mlflow, Domino, Triton Inference Server, Hugging Face

· Methodology and Process

o Scrum, Kanban, Lean, SRE, Git Ops

· Automation Tools

o Puppet, Chef, Ansible, Terraform, Cloud Formations

· Machine and Deep Learning languages, tools, and libraries

o Python, Tensor

RT, Pytorch, Keras, Scikit-learn, CUDA, Pandas

· No

Sql Databases

o Cassandra, Mongo

DB, Redis, Vector Databases