MLOPS - Consultant

Job Title: MLOps Consultant
Job Type: Full-time/Contract 6 Months with possible extension
Job Location: Remote

Responsibilities
We are seeking a highly skilled and motivated Senior Principal Analyst to join our team. The ideal candidate will possess a strong technical background with expertise in MLOps, coupled with exceptional business acumen and communication skills. As a Senior Principal Analyst, you will be responsible for leading technical initiatives, and providing expert consultation to our clients.

Key Responsibilities
Design the data pipelines and engineering infrastructure to support our clients enterprise machine learning systems at scale
Take offline models data scientists build and turn them into a real machine learning production system
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients machine learning systems
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Support model development, with an emphasis on auditability, versioning, and data security
Facilitate the development and deployment of proof-of-concept machine learning systems
Communicate with clients to build requirements and track progress

Qualifications
Total Experience - 6 to 15 years
Relevant Experience - 5+ years
Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
Mandatory Skills - Azure, Python, Pandas, NumPy, Scikit-learn, Jupyter Notebook, SQL, Kubernetes, Kubeflow/Airflow, Docker, Nginx, ML Pipeline build, Model retraining, Model deployment, monitoring and maintenance, CI/CD, Tensorflow, PyTorch
Good to have - EKS / GKE, VertexAI
Strong software engineering skills in complex, multi-language systems
Comfort with Linux administration
Experience working with cloud computing and database systems
Experience building custom integrations between cloud-based systems using APIs
Experience developing and maintaining ML systems built with open source tools
Experience developing with containers and Kubernetes in cloud computing environments
Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
Ability to translate business needs to technical requirements
Strong understanding of software testing, benchmarking, and continuous integration
Exposure to machine learning methodology and best practices
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)