Job
Description
In this role, you will drive the end-to-end development and deployment of AI/ML solutions on Cloud, applying your expertise in machine learning, MLOps, and GenAI. You will mentor junior engineers by establishing best practices and guiding solution design. Additionally, you will design, implement, and optimize advanced machine learning algorithms using robust data science techniques, while driving the scaling and adoption of AI/ML technologies to meet strategic business objectives. If you have a passion for AI, cloud-based ML solutions, and driving scalable innovation, this role is for you. You have: Bachelors or masters degree in computer science, Engineering, or related field with 8+ years of experience in machine learning, data science, and statistics. Expertise in ML algorithms with strong Python and SQL skills, including Jupyter notebooks. Proven knowledge of GenAI and Agentic AI technologies. Hands-on experience with MLOps tools like Kubeflow, Vertex AI, and GenAI/LLMops. Grip on, Kubernetes, Docker, and microservices architecture. It would be nice if you also had: Experience with OpenAI, Llama, and Lang Chain frameworks. Practical experience in Pytest and Rust programming. Prior experience developing AI applications in the telecom domain. Google Cloud Professional Machine Learning Engineer certification. Architect and develop AI/ML use cases on Cloud, driving projects from discovery and proof of concept through industrialization, deployment, and ongoing maintenance. Mentor and guide junior engineers in defining robust methodologies and evaluating the most effective technical solutions. Propose, implement, test, and select optimal machine learning algorithms by leveraging advanced statistical tools and data science techniques to uncover insights and generate accurate predictions. Drive the rapid scaling and integration of AI and ML technologies to enhance system performance and support strategic business objectives.
