AI Pipeline Engineer

2 days ago

Hyderabad, Telangana, India beBeeMLOps Full time ₹ 1,20,00,000 - ₹ 2,00,00,000
Job Title

We are seeking a highly skilled and experienced ML Ops / LLM Engineer to join our team. You will play a crucial role in building and maintaining the infrastructure and pipelines for Generative AI applications, working closely with the Full Stack Architect.

  • Design, develop, and implement ML/LLM pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
  • MLOps Support and Maintenance on ML Platforms Dataiku/Sagemaker.
  • Apply understanding of Dataiku Govern functionalities, including item and artifact management, workflow control, and sign-off processes.
  • Using best practices for data governance and model accountability within the MLOps lifecycle.
  • Automate ML tasks across the model lifecycle, leveraging tools like GitOps, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes).
  • Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows.
  • Design and implement monitoring and alerting systems to track model performance, data drift, and other key metrics.
  • Conduct ground truth analysis to evaluate the accuracy and effectiveness of LLM outputs compared to known, correct data.
  • Work closely with infrastructure and DevOps teams to provision and manage resources for ML and LLM development and deployment.
  • 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, 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.
Required Skills and Qualifications
  • Minimum Qualifications:
  • Bachelor's degree in computer science, Data Science, Engineering, or a related field, or equivalent experience.
  • Experience in MLOps or related areas, such as DevOps, data engineering, or ML infrastructure.
  • Knowledge of best practices for data governance and model accountability within the MLOps lifecycle.
  • Covered these Dataiku Certifications: ML Practitioner Certificate, Advanced Designer Certificate, and MLOps Practitioner Certificate.
  • Proven experience in ML Ops, LLM Ops, or related roles, with hands-on experience deploying and managing machine learning and large language model pipelines.
  • 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.

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