MLOPS Architect

AppLab Systems, Inc; Tempa (WV); Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a skilled MLOps Architect to design and implement cloud architectures for machine learning applications. This role involves collaborating with cross-functional teams to establish best practices in MLOps, including the development of data pipelines and CI/CD processes. You will leverage your expertise in AWS and big data technologies to optimize performance and ensure compliance with security standards. This position offers the chance to work on cutting-edge projects in a dynamic environment, making a significant impact on the future of AI-driven solutions. Join a forward-thinking team dedicated to pushing the boundaries of technology.

Qualifications

• Strong experience in Python and cloud computing platforms, especially AWS.
• Experience with AI workflow management tools and statistical modeling.

Responsibilities

• Design cloud architectures for MLOps implementations with a focus on scalability.
• Develop data pipelines for efficient data management and processing.

Skills

Python

Data product development

Analytical models

Model governance

AI workflow management tools

Statistical modeling

Machine learning algorithms

Predictive analytics

Agile practices

Cloud computing platforms

Tools

AWS

Airflow

Kedro

Luigi

Hadoop

Spark

AWS CloudFormation

Terraform

Job description

Hi,

Hope you are doing good…!!

I am Baiju represented to Applab Systems and I have an excellent job opportunity for you. Please find the below job description and if you are comfortable, please reply or contact me for further discussion.

Job Title: MLOPS Architect (Machine Learning / AI Architect)

Location: Remote

Job Type: 6-12+Months

Job Description

Key Technologies: AWS, Python, Airflow, Kedro, or Luigi; Hadoop, Spark, or similar frameworks. Experience with graph databases a plus.

Responsibilities

1. Designing Cloud Architecture:
   As an AWS Cloud Architect, you'll be responsible for designing cloud architectures, preferably on AWS, Azure, or multi-cloud environments. Your architecture design should enable seamless scalability, flexibility, and efficient resource utilization for MLOps implementations.
2. Data Pipeline Design:
   Develop data taxonomy and data pipeline designs to ensure efficient data management, processing, and utilization across the AI/Client platform. These pipelines are critical for ingesting, transforming, and serving data to machine learning models.
3. MLOps Implementation:
   Collaborate with data scientists, engineers, and DevOps teams to implement MLOps best practices. This involves setting up continuous integration and continuous deployment (CI/CD) pipelines for model training, deployment, and monitoring.
4. Infrastructure as Code (IaC):
   Use tools like AWS CloudFormation or Terraform to define and provision infrastructure resources. Infrastructure as Code allows you to manage your cloud resources programmatically, ensuring consistency and reproducibility.
5. Security and Compliance:
   Ensure that the MLOps architecture adheres to security best practices and compliance requirements. Implement access controls, encryption, and monitoring to protect sensitive data and models.
6. Performance Optimization:
   Optimize cloud resources for cost-effectiveness and performance. Consider factors like auto-scaling, load balancing, and efficient use of compute resources.
7. Monitoring and Troubleshooting:
   Set up monitoring and alerting for the MLOps infrastructure. Be prepared to troubleshoot issues related to infrastructure, data pipelines, and model deployments.
8. Collaboration and Communication:
   Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders. Effective communication is essential to align technical decisions with business goals.

Minimum Requirements

• Strong experience in Python
• Experience in data product development, analytical models, and model governance
• Experience with AI workflow management tools such as Airflow, Kedro, or Luigi
• Exposure to statistical modeling, machine learning algorithms, and predictive analytics.
• Highly structured and organized work planning skills
• Strong understanding of the AI development lifecycle and Agile practices
• Proficiency in big data technologies like Hadoop, Spark, or similar frameworks. Experience with graph databases a plus.
• Extensive experience in working with cloud computing platforms - AWS
• Proven track record of delivering data products in environments with strict adherence to security and model governance standards.

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Thanks & Regards,

Baiju Kumar

[e-mail]

[phone]

4365 Route 1 South, Suite 105, Princeton, NJ-08540