Principal ML Ops Architect

Principal ML Ops Architect
Responsibilities:
1. Strategic Leadership:
a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.
b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes.
c. Foster a culture of innovation and continuous improvement within the ML Ops team.
2. Technical Architecture:
a. Design and implement scalable, reliable, and efficient ML Ops architectures.
b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle.
c. Ensure compliance with industry best practices and standards for ML Ops.
3. Team Management:
a. Lead and mentor a team of ML Ops engineers and architects.
b. Foster collaboration and knowledge sharing among team members.
c. Provide technical guidance and support to data scientists and engineers.
4. Innovation and Research:
a. Stay up-to-date with emerging ML Ops trends and technologies.
b. Research and evaluate new tools and techniques to enhance ML Ops capabilities.
c. Contribute to the development of innovative ML Ops solutions.
Minimum Required Skills:
11+ years of experience preferred.
Proven track record of designing and implementing large-scale ML pipelines and infrastructure.
Experience with distributed computing frameworks (Spark, Hadoop)
Knowledge of graph databases and auto ML libraries
Bachelor's / Masters degree in computer science, analytics, mathematics, statistics
Strong experience in Python, SQL.
Solid understanding and knowledge of containerization technologies (Docker, Kubernetes).
Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow)
Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch).
Certifications in cloud platforms or ML technologies can be a plus.
Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
Strong problem-solving and analytical skills.
Ability to plan, execute and take ownership of task.
Keywords
ML Ops / MLOps Architect
Azure DevOps
Docker
Kubernetes
TensorFlow
MLFlow
Pipeline
Machine Learning Platform Engineer
Data Science Platform Engineer
DevOps Engineer (with ML focus)
AI Engineer
Data Engineer
Cloud Engineer (with ML focus)
Software Engineer (with ML focus)
Model Deployment Specialist
MLOps Architect
CI/CD
PyTorch
Scikit-learn
Cloud Computing
Big Data
Azure
o Azure Machine Learning
GCP
o Vertex AI
AWS
o Amazon SageMaker

Keywords
ML Ops / MLOps Architect, Azure DevOps, Docker, Kubernetes, TensorFlow, ML Flow ,Pipeline, Machine Learning Platform Engineer, Data Science Platform Engineer, DevOps Engineer (with ML focus),AI Engineer, Data Engineer, Cloud Engineer (with ML focus),Software Engineer (with ML focus),Model Deployment Specialist, MLOps Architect, CI/CD, PyTorch, Scikit-learn, Cloud Computing, Big Data, Azure, Azure Machine Learning, GCP, Vertex AI, AWS, Amazon SageMaker.

Location : - Across Multiple locations