ML ops Engineer

Role Overview: Join a leading global credit rating and financial analytics powerhouse leveraging cutting-edge AI and analytics to deliver state-of-the-art solutions. As a ML Ops Engineer at GenAI & ML Solutions, you will play a crucial role in developing and managing efficient MLOps pipelines tailored for Large Language Models, automating deployment and lifecycle management of models in production. Key Responsibilities: - Develop and manage efficient MLOps pipelines for Large Language Models, automating deployment and lifecycle management in production. - Deploy, scale, and monitor LLM inference services across cloud-native environments using Kubernetes, Docker, and other container orchestration frameworks. - Optimize LLM serving infrastructure for latency, throughput, and cost, including hardware acceleration setups with GPUs or TPUs. - Build and maintain CI/CD pipelines specifically for ML workflows, enabling automated validation and seamless rollouts of continuously updated language models. - Implement monitoring, logging, and alerting systems to track model performance, resource utilization, and system health. - Collaborate cross-functionally with ML research and data science teams to operationalize fine-tuned models and multi-agent LLM workflows. - Integrate LLMs with APIs and downstream applications, ensuring reliability, security, and compliance with data governance standards. - Troubleshoot complex operational issues impacting model availability and degradation, implementing fixes and preventive measures. - Stay up to date with emerging trends in LLM deployment, optimization techniques, and evolving MLOps best practices. Qualifications Required: - 3 to 5 years of professional experience in Machine Learning Operations or ML Infrastructure engineering, including deploying and managing large-scale ML models. - Proficiency in Python and scripting languages such as Bash for workflow automation. - Hands-on experience with containerization and orchestration technologies like Docker and Kubernetes. - Experience with cloud platforms (AWS, Google Cloud Platform, Azure) and serving models using frameworks like Hugging Face Transformers or OpenAI APIs. - Familiarity with CI/CD pipelines tuned to ML lifecycle workflows and performance optimization techniques for large-scale transformer models. - Knowledge of monitoring and logging technologies to ensure production-grade observability. - Strong problem-solving skills and effective communication to work in a fast-paced environment. (Note: Educational Background and Benefits sections have been omitted from the Job Description as they do not directly relate to the job responsibilities and qualifications),