This role offers the opportunity to leverage your cloud-native deployment skills and experience with modern AI platforms to build scalable ML pipelines, LLM-based applications, and intelligent agent frameworks. You will play a key role in accelerating the delivery of innovative solutions for telecom, enterprise, and next-generation autonomous networks. If you thrive in a challenging and rewarding environment, we encourage you to apply.

You have:

Bachelor's/Master's degree in computer science, Data Engineering, AI/ML, or related field.10+ years in AI/ML engineering, with 5+ years focused on MLOps.Proficient in Python, PyTorch, TensorFlow, Scikit-learn, and SQL.Experience with MLOps pipelines like Kubeflow, MLflow, and Vertex AI.

It would be nice if you also had:

Experience with Ab-initio data management platforms.Familiarity with telecom data products and autonomous networks.Knowledge of vector databases and retrieval—augmented generation (RAG).Contributions to open-source AI/ML or GenAI frameworks.Employ MLOps best practices for CI/CD, model deployment, and monitoring.Focus on fine-tuning, prompt engineering, RAG pipelines, and LLM monitoring.Enhance AI/ML systems with Agentic AI frameworks for autonomous decision-making and workflow automation.Handle structured, semi-structured, and unstructured data through ingestion, preprocessing, and feature engineering.Work with cross-functional teams to translate use cases into production-ready deployments.Utilize platforms like GCP (Vertex AI), RedHat OpenShift AI, and Kubeflow across multi-cloud/hybrid environments.