Intellibus
Lead ML Ops Engineer
Intellibus, Reston, Virginia, United States, 22090
Job Description
Are you a MLOps Engineer working at a Large Financial Institution and being told by your leadership that you are too hands-on or detail-oriented or think and work like a start-up?
Imagine working at
Intellibus
to engineer platforms that impact billions of lives around the world. With your passion and focus we will accomplish great things together!
We are looking forward to you joining our Platform Engineering Team.
Our Platform Engineering Team is working to solve the Multiplicity Problem. We are trusted by some of the most reputable and established FinTech Firms. Recently, our team has spearheaded the Conversion & Go Live of apps which support the backbone of the Financial Trading Industry.
We are looking for someone who can
Collaborate with stakeholders to define MLOps strategies aligned with business objectives and technical requirements. Assess current infrastructure, processes, and tooling to identify gaps and opportunities for MLOps implementation.
Design, Develop, and Implement end-to-end ML deployment pipelines for model training, perform validation, deployment, and monitoring. Automate data ingestion, feature engineering, model training, and evaluation processes using tools like Apache Airflow, Kubeflow, or MLflow.
Architect and deploy scalable infrastructure for ML workloads using cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).
Implement infrastructure as code (IaC) practices for provisioning and managing ML infrastructure using tools like Terraform or AWS CloudFormation.
Deploy ML models into production environments using containerized solutions and orchestration platforms.
Implement model monitoring and logging solutions to track model performance, data drift, and model drift in production.
Perform Integration and Deployment (CI/CD), Establish CI/CD pipelines for automated testing, validation, and deployment of ML models using tools like Jenkins, GitLab CI/CD, or CircleCI.
Implement version control and model versioning practices to manage changes and updates to ML models.
Implement security best practices for securing ML infrastructure, data, and models in compliance with regulatory requirements. Establish governance policies and access controls for managing and monitoring ML artifacts and resources.
Provide training and mentorship to data scientists, engineers, and stakeholders on MLOps practices, tools, and methodologies. Foster a culture of collaboration and continuous improvement in MLOps adoption across the organization.
Work closely with clients to understand their MLOps needs, assess their current ML infrastructure, and recommend solutions for MLOps implementation. Provide clients strategic guidance and technical expertise in adopting MLOps practices and optimizing their ML deployment pipelines.
Qualifications
Bachelor's degree in Computer Science, or a related field is preferred. Relevant work experience may be considered in lieu of a degree.
Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and stakeholders.
Proven leadership abilities, with experience mentoring junior developers and driving technical excellence within the team.
Experience Needed
At least 7 years of Data Wrangling Experience
At least 7 years of MLOps Experience
At least 7 years of ETL Experience
Our Process
Schedule a 15 min Video Call with someone from our Team
4 Proctored GQ Tests ( 30-45 min Final Video Interview
Receive Job Offer
If you are interested in reaching out to us, please apply and our team will contact you within the hour.
#J-18808-Ljbffr
Imagine working at
Intellibus
to engineer platforms that impact billions of lives around the world. With your passion and focus we will accomplish great things together!
We are looking forward to you joining our Platform Engineering Team.
Our Platform Engineering Team is working to solve the Multiplicity Problem. We are trusted by some of the most reputable and established FinTech Firms. Recently, our team has spearheaded the Conversion & Go Live of apps which support the backbone of the Financial Trading Industry.
We are looking for someone who can
Collaborate with stakeholders to define MLOps strategies aligned with business objectives and technical requirements. Assess current infrastructure, processes, and tooling to identify gaps and opportunities for MLOps implementation.
Design, Develop, and Implement end-to-end ML deployment pipelines for model training, perform validation, deployment, and monitoring. Automate data ingestion, feature engineering, model training, and evaluation processes using tools like Apache Airflow, Kubeflow, or MLflow.
Architect and deploy scalable infrastructure for ML workloads using cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).
Implement infrastructure as code (IaC) practices for provisioning and managing ML infrastructure using tools like Terraform or AWS CloudFormation.
Deploy ML models into production environments using containerized solutions and orchestration platforms.
Implement model monitoring and logging solutions to track model performance, data drift, and model drift in production.
Perform Integration and Deployment (CI/CD), Establish CI/CD pipelines for automated testing, validation, and deployment of ML models using tools like Jenkins, GitLab CI/CD, or CircleCI.
Implement version control and model versioning practices to manage changes and updates to ML models.
Implement security best practices for securing ML infrastructure, data, and models in compliance with regulatory requirements. Establish governance policies and access controls for managing and monitoring ML artifacts and resources.
Provide training and mentorship to data scientists, engineers, and stakeholders on MLOps practices, tools, and methodologies. Foster a culture of collaboration and continuous improvement in MLOps adoption across the organization.
Work closely with clients to understand their MLOps needs, assess their current ML infrastructure, and recommend solutions for MLOps implementation. Provide clients strategic guidance and technical expertise in adopting MLOps practices and optimizing their ML deployment pipelines.
Qualifications
Bachelor's degree in Computer Science, or a related field is preferred. Relevant work experience may be considered in lieu of a degree.
Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and stakeholders.
Proven leadership abilities, with experience mentoring junior developers and driving technical excellence within the team.
Experience Needed
At least 7 years of Data Wrangling Experience
At least 7 years of MLOps Experience
At least 7 years of ETL Experience
Our Process
Schedule a 15 min Video Call with someone from our Team
4 Proctored GQ Tests ( 30-45 min Final Video Interview
Receive Job Offer
If you are interested in reaching out to us, please apply and our team will contact you within the hour.
#J-18808-Ljbffr