Experienced with ML frameworks like TensorFlow, PyTorch, or Scikit-learn
Expertise in AWS, particularly Amazon SageMaker
In-depth knowledge of MLOps tools like Docker, Kubernetes, and Jenkins
Understanding of data engineering concepts and tools like SQL and ETL pipelines
Responsibilities
Design and implement robust MLOps pipelines
Develop and maintain CI/CD pipelines for ML models
Build and manage ML infrastructure on cloud platforms
Optimize model performance and resource utilization
Monitor model performance for improvement opportunities
Collaborate with data scientists to enhance ML development processes
Ensure data quality and integrity throughout the ML pipeline
Benefits
Flexible work location with options in Holmdel, Bethlehem, or New York
Requires in-office presence two days a week
Open to H-1 visa candidates, but not OPT
Lead position with potential for working with senior resources
Full Job Description
We are seeking a Lead ML OPs Engineer who is a self-starter and can work independently to implement and operate new technologies that can help the Company advance its Automation & Artificial Intelligence objectives.
Driven by a desire to deliver extraordinary customer service and a rock-solid stable AI platform, you will be successful through successful operation of our AI platforms.
Location is Holmdel, Bethlehem, or New York. 2 days a week in office
No relocation, MUST reside near an office
H-1's ok, NO OPT
This is a lead position. no direct reports but a Senior resource.
As a Lead MLOps Engineer, you will play a pivotal role in building and maintaining the Company's machine learning (ML) infrastructure. In this position, you will collaborate closely with data scientists and engineers to deploy and lead ML models to production. The responsibilities will include:
Designing and implementing robust MLOps pipelines to streamline the ML lifecycle, from data ingestion to model deployment and monitoring.
Developing and maintaining CI/CD pipelinesfor ML models, ensuring efficient and reliable deployment.
Building and managing ML infrastructure on cloud platforms, with a focus on Amazon SageMaker.
Optimizing model performance and resource utilization in production environments.
Monitoring model performance and finding opportunities for improvement.
Collaborating with data scientists and engineers to improve ML model development processes.
Ensuring data quality and integrity throughout the ML pipeline.
Staying up-to-date with the latest advancements in MLOps and machine learning.
Level of experience required:
A minimum of 7 Years of experience Ops Engineering and 4 years of Machine Learning experience.
Strong proficiency in Python programming language.
Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Expertise in cloud platforms, particularly Amazon Web Services (AWS) and Amazon SageMaker.
In-depth knowledge of MLOps tools and technologies (e.g., Docker, Kubernetes, Jenkins, Airflow).
Experience with version control systems (Git).
Understanding of data engineering concepts and tools (e.g., SQL, ETL pipelines).
Proficiency in cloud-based data storage and processing services (e.g., S3, EMR, Redshift).
Knowledge of big data technologies is a plus.
Have knowledge about data engineering concepts, tools and automation processes (DataOps) since data pipelines and architectures provide the base for building AI solutions.
Strong problem-solving and analytical skills.
Excellent communication and collaboration skills.
Ability to work independently and as part of a team.
Attention to detail and focus on quality.
Passion for machine learning and data science.
A continuous learner with a desire to stay updated on the latest industry trends.
