Infojini Inc ยท 1 hour ago
Machine Learning Operations Engineer
United States
Full-time
Remote
Mid Level
3+ years expInfojini Inc is seeking a Machine Learning Operations Engineer to manage the full lifecycle of machine learning models. The role involves collaborating with data scientists and clinical operations to deploy and maintain AI solutions that enhance patient care and operational excellence.
Information Technology
Hiring Manager
Nooman Idrisi
Responsibilities
Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure
Ability to lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions
Experience in developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements
Demonstrated ability to collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models
Expertise in implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes
Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance
Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration
Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations
Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations
Qualification
Required
Bachelor's degree in computer science, artificial intelligence, informatics or closely related field
3 or more years relevant Machine Learning Engineer Experience
Proven experience with: Artificial intelligence and machine learning platforms (e.g., AWS, Azure or GCP)
Containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes)
CI/CD tools (e.g., Github Actions)
Programming languages and frameworks (e.g., Python, R, SQL)
Deep understanding of coding, architecture, and deployment processes
Strong understanding of critical performance metrics
Extensive experience in predictive modeling, LLMs, and NLP
Exhibit the ability to effectively articulate the advantages and applications of the RAG framework with LLMs
Experience in managing end-to-end ML lifecycle
Experience in managing automation with Terraform
Production Deployment and Model Engineering: Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
Scalable ML Infrastructures: Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure
Engineering Leadership: Ability to lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions
AI Pipeline Development: Experience in developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements
Collaboration: Demonstrated ability to collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models
Continuous Integration/Continuous Deployment (CI/CD) Pipelines: Expertise in implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes
Monitoring and Logging: Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance
Version Control: Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration
Security and Compliance: Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations
Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations
Preferred
Master's degree in computer science, engineering or closely related field preferred
Company
Infojini Inc
Infojini Inc. delivers innovative IT Services and Solutions to empower startups, Fortune 500 companies, and global enterprises.
501-1000 employees
H1B Sponsorship
Infojini Inc has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (6)
2024 (12)
2023 (11)
2022 (5)
2021 (10)
2020 (21)
Funding
Current Stage
Late StageRecent News
Baltimore Business Journal
2023-11-04
Company data provided by crunchbase