Ml Ops Engineer Job in Vconstruct

Essential Functions and Responsibilities Develop and maintain scalable ML infrastructure and tools to support the deployment, monitoring, and management of ML models in production. Collaborate with data scientists and software engineers to design, implement, and optimize ML workflows and pipelines. Automate and streamline ML processes, including model training, evaluation, deployment, and monitoring. Innovative mindset to create high performing deployment pipelines based on usecases. Implement CI/CD (Continuous Integration/Continuous Deployment) practices for ML systems, ensuring smooth and reliable deployments. Monitor and troubleshoot ML systems to identify and resolve performance issues, bottlenecks, and failures. Design and implement robust data pipelines for data ingestion, preprocessing, and feature engineering to support ML training and inference. Ensure data integrity, security, and compliance in ML systems, adhering to relevant regulations and best practices. Stay up to date with the latest advancements in ML Ops, DevOps, and cloud technologies, and propose innovative solutions to enhance our ML infrastructure. Collaborate with cross-functional teams to define and enforce ML Ops best practices, standards, and guidelines. Skills and Qualifications Bachelor's or Master's degree in computer science, data science, or a related field. Proven experience in ML Ops, DevOps, or related roles, with a focus on deploying and operating ML models in production. Experience with machine learning frameworks and techniques, including model training, evaluation, and optimization. Knowledge of big data processing technologies (e.g., Apache Spark) and distributed computing concepts. Understanding of security and privacy considerations in ML systems, including data anonymization and encryption. Familiarity with AIOps (Artificial Intelligence for IT Operations) and monitoring tools for ML systems. Technical Skills Strong programming skills in languages such as Python, and proficiency in using ML frameworks and libraries (e.g., TensorFlow, PyTorch). Experience with containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP). Solid understanding of DevOps principles, CI/CD practices, and automation tools (e.g., Jenkins, GitLab CI/CD). Familiarity with version control systems (e.g., Git) and infrastructure-as-code frameworks (e.g., Terraform). Knowledge of data management and storage technologies (e.g., SQL, NoSQL, Apache Kafka). Strong problem-solving skills and the ability to analyze and resolve complex technical issues in ML systems. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. Experience with machine learning frameworks and techniques, including model training, evaluation, and optimization. General Ability to multi-task Ability to work in a collaborative team environment Strong communication (oral and written) and interpersonal skills required to interact with colleagues and internal customers. Excellent at troubleshooting issues Ability to develop productive business relationships with internal team members through cooperation, courtesy and professionalism Ability to play an integral part in project delivery given tight constraints and uncompromising quality Motivated to identify and develop solutions leveraging best practices Capable of explaining complex technical issues to clients and internal resources