Telecom AI Specialist

5 days ago

madurai, India beBeeArchitecture Full time

AI ArchitectAbout This RoleWe are seeking a seasoned AI architect to spearhead the design and development of cutting-edge artificial intelligence solutions for telecom and networking use cases.15+ years of total experience with at least 10 years in Telecom (4G/5G & IMS) and/or Datacom (Routing, Switching / SD-WAN / Provider Edge)8+ years of hands-on experience with ML Algorithms; Graph Neural Networks, Time-series Forecasting Algorithms (ARIMA, LSTM…)Solid programming skills in Python and proficiency with ML/DL libraries (PyTorch, TensorFlow, Scikit-learn)Strong experience with LLM frameworks (LangChain, Hugging Face, Crew AI, OpenAI APIs, etc.)Experience in Agentic AI, autonomous multi-agent systems and RAG pipelinesProven experience building multiple AI/ML solutions for Telecom and Networking use casesNeed to bring proven communication skills and mentor cross-functional teams on the architecture, use cases and drive architectural direction of the productsProven track record in Design and deployment of data pipelines on GCP (BigQuery, Dataflow, Dataproc, Pub/Sub) for batch and real-time streaming. Experience in dealing with carrier class data sets of terabyte-scaleProven experience deploying ML systems on AWS and DatabricksTrack record in building scalable AI infrastructure using Kubernetes, Docker, Terraform, EKS, automating deploymentsExperience with vector databases (FAISS, Pinecone, Weaviate, etc.)Exposure to MLOps frameworks (MLflow, Kubeflow, Airflow)Strong communication skills with ability to translate technical results into business insightsBenefitsThis role offers the opportunity to work on innovative projects, collaborate with experienced professionals, and develop your expertise in AI and machine learning.What You'll Be DoingSpearheading AI architecture for telecom and networking use casesDesigning and developing AI solutions using ML algorithms and frameworksMentoring cross-functional teams on AI architecture and use casesDriving architectural direction of AI productsDeploying data pipelines on GCP and AWSBuilding scalable AI infrastructure using Kubernetes and Docker

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