Head of AI

floLIVE is rewriting the playbook for the global IoT Connectivity landscape. Our groundbreaking Connectivity Management Service is reshaping the way Enterprises, Cloud providers, IoT service providers, and Mobile Operators connect and manage their devices across the globe.

Our global presence includes the United Kingdom, the United States, Israel, Bulgaria, and China, solidifying our commitment to local, secure, and compliant connectivity solutions. With our innovative software-defined connectivity technology, FloLIVE delivers seamless connectivity management across boundaries

Position Overview
As a Data Scientist at FloLIVE, you will lead the development of groundbreaking AI solutions at the intersection of mobile networks, cybersecurity, and cloud-native IoT connectivity. This is a rare opportunity to build next-generation AI systems- from lightweight ML to deep learning and LLM-driven intelligence — in a global telecom context. You'll join a team pushing boundaries in behavior classification, anomaly detection, and semantic intelligence across real-time telecom data streams.

Why FloLIVE?
At FloLIVE, we don’t just build telecom platforms — we rethink how intelligence flows through global connectivity. If you're excited to pioneer AI in mobile networks, this is your launchpad.

Responsibilities

Develop, optimize, and deploy AI/ML models for anomaly detection, behavior profiling, and predictive analytics across mobile network traffic and IoT telemetry.
Apply state-of-the-art deep learning and unsupervised learning techniques (e.g., autoencoders, embeddings, transformers).
Leverage foundation models and LLMs to interpret structured logs, extract behavioral narratives, and enable synthetic data generation.
Build efficient real-time pipelines for high-volume event streams — from the cloud to the edge.
Partner with engineering and product teams to integrate models seamlessly into FloLIVE’s cloud-native architecture.
Ensure explainability, robustness, and scalability of AI pipelines across a global telecom environment.

Qualifications

B.Sc. in Computer Science, Statistics, or similar; M.Sc. or Ph.D. preferred.
3+ years of experience in applied data science, preferably in networked systems, cybersecurity, or telecom analytics.
Strong foundation in classical ML and deep learning (e.g., tree-based models, CNNs, LSTMs, Transformers).
Proven experience with unsupervised learning and time-series or event-sequence modeling.
Skilled in Python, ML/DL frameworks (scikit-learn, PyTorch, TensorFlow), and working with large, noisy datasets.
Deep understanding of statistics, probability, and model evaluation techniques.

Nice to Have

Experience with 3GPP stack protocols (MAP, Diameter, GTP, NAS) or SIM lifecycle events.
Experience with embeddings, clustering, autoencoders, or contrastive learning for behavior modeling.
Familiarity with cloud-native AI deployments in AWS, GCP, Azure, MCP, or OCI.
Exposure to LLMs (e.g., GPT, Claude, BERT, LangChain) for semantic analysis, summarization, or anomaly explanation.
Publications, patents, or open-source contributions in AI for infrastructure, mobility, or security domains.

Tools & Technologies
Python, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers (HuggingFace)
Clickhouse, Spark, Kafka, MLflow, Docker, Airflow
Cloud: AWS, GCP, Azure, MCP, OCI
LLM/AI: OpenAI APIs, LangChain, Sentence Transformers, Autoencoders