ML OPS Engineer

Role: ML OPS Engineer

Type: Full Time.

Location: Philadelphia - PA

Flexible work from home days
Location : Pennsylvania - PA Candidates should be located in EST Time Zone OR CST Zone. Need to Travel to the above location 2-3 weeks initially to understand the project after that 3-4 days in a month.

Summary:

We are seeking a highly skilled and experienced MLOps Engineer to join our team in USA. You will play a crucial role in building and maintaining the infrastructure and pipelines for our cutting-edge Generative AI applications, working closely with the Generative AI Full Stack Architect . Your expertise in automating and streamlining the ML lifecycle will be instrumental in ensuring the efficiency, scalability, and reliability of our Generative AI models in production.

Responsibilities:

• Design, develop, and implement MLOps pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
• Automate ML tasks across the model lifecycle, leveraging tools like GitOps, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes).
• Develop and maintain robust monitoring and alerting systems for generative AI models in production, ensuring proactive identification and resolution of issues.
• Collaborate with the Generative AI Full Stack Architect and other engineers to optimize model performance and resource utilization.
• Manage and maintain cloud infrastructure (e.g., AWS, GCP, Azure) for ML workloads, ensuring cost-efficiency and scalability.
• Stay up-to-date on the latest advancements in MLOps and incorporate them into our platform and processes.
• Communicate effectively with technical and non-technical stakeholders about the health and performance of generative AI models.

Qualifications:

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
• 8+ years of experience in MLOps or related areas, such as DevOps, data engineering, or ML infrastructure.
• Proven experience in automating ML pipelines with tools like MLflow, Kubeflow, Airflow, etc.
• Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads.
• Strong understanding of CI/CD principles and containerization technologies like Docker and Kubernetes.
• Familiarity with monitoring and alerting tools for ML systems (e.g., Prometheus, Grafana).
• Excellent communication, collaboration, and problem-solving skills.
• Ability to work independently and as part of a team.
• Passion for Generative AI and its potential to revolutionize various industries.
• Senior individual contributor with significant expertise and leadership experience.
• Manages complex projects and initiatives with independent decision-making authority.
• Provides technical guidance and mentoring to junior team members.
• Has a proven track record of success in delivering impactful results.