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.
