職務内容：

We are a $13+ billion global technology company, home to more than 224,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud, and AI, powered by a broad portfolio of technology services and products.

HCLTech is a globally recognized leader in the Tech and IT industry, but we’ve never forgotten the startup mindset that got us here. We’ve always approached our work with an idea-first attitude because every one of our accomplishments —no matter how big or small —can be traced back to an idea’s single spark.

It’s that spark —that inner drive —that sets our people apart from our competitors. It enables us not just to pull off game-changing feat after game-changing feat but to better our world in the process. We want you to find your spark. Because that’s what drives you to be better, be more and ultimately, be more fulfilled.

Responsibilities:

• Develop and deploy end-to-end microservices-based solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance testing.
• Design, implement, and optimize MLOps pipelines using tools such as Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.
• Collaborate with Data Scientists to enhance the ML model development process and ensure performance improvements.
• Ensure scalability, maintainability, and robustness of deployed machine learning models.
• Monitor and troubleshoot ML model performance and infrastructure issues in production (experience with Prometheus and Grafana is valuable).
• Support and enhance ML software infrastructure, including CI/CD, data storage, cloud services, security, and system monitoring.
• Work with cloud platforms, particularly GCP and Azure, to optimize resource allocation and costs.
• Stay up to date with the latest trends and best practices in MLOps.

Qualifications:

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• 3+ years of experience as a Machine Learning Engineer or in a similar role.
• Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn.
• Strong understanding of MLOps best practices and tools, including Kubeflow, Seldon, MLFlow, Docker, and Kubernetes.
• Experience working with cloud platforms, especially GCP.
• Knowledge of data processing, ETL, and feature engineering techniques.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
• Excellent communication and interpersonal skills.

Preferred Qualifications:

• Experience deploying ML models at scale using serverless or cloud-based solutions.
• Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly).
• Knowledge of software development best practices (Git, CI/CD, automated testing).