Senior ML Ops Engineer

Job Description

We are looking for experienced ML Ops Engineer with 3+ years of experience, specializing in Machine Learning. In this role, you will be responsible for developing ML infrastructure around LLMs/ML Models. The ideal candidate will possess a deep understanding of the ML lifecycle and infrastructure. Key Responsibilities: Collaboration: Work closely with data scientists and ML engineers throughout the ML lifecycle, supporting model development, deployment, and monitoring. ML Ops Pipeline Development: Design, implement, and optimize ML Ops pipelines using tools and frameworks such as TensorFlow Serving, Kubeflow, MLflow, or similar technologies. Data Pipeline Engineering: Build and maintain data pipelines and infrastructure necessary for enterprise-scale machine learning applications, focusing on tasks like data ingestion, preprocessing, transformation, feature engineering, and model training. Cloud Implementation: Develop cloud-based ML Ops solutions on cloud platforms like AWS, Azure, GCP. Containerization Skills: Familiarity with containerization technologies like Docker and Kubernetes. Model Deployment and Monitoring: Deploy and monitor various machine learning models in production, including text/NLP and generative AI models. CI/CD Automation: Build and maintain CI/CD pipelines using tools such as GitLab CI, GitHub Actions, or Airflow to streamline the ML lifecycle. Model Review and Quality Assurance: Participate in data science model reviews, focusing on code optimization, containerization, deployment, versioning, and quality monitoring. Support Data Model Development: Contribute to data model development with an emphasis on auditability, versioning, and data security, implementing practices like lineage tracking and model explainability. Mentorship: Provide guidance and support to junior engineers, fostering a collaborative and innovative team environment. Experience: Minimum of 3+ years of relevant work experience in ML Ops. Domain Knowledge: Strong expertise in Generative AI, advanced NLP, and machine learning techniques. Production Experience: Proven experience in deploying and maintaining production-grade AI solutions. Communication Skills: Excellent communication and teamwork skills, with the ability to work independently when needed. Problem-Solving: Strong analytical and problem-solving capabilities. Continuous Learning: Stay informed about the latest advancements in ML Ops technologies and actively explore new tools and techniques to improve system performance and reliability.