About this role
Join our Team
About the Opportunity:
Ericsson Enterprise Wireless Solutions (BEWS) drives Ericsson's Enterprise Networking and Security business. Our growing portfolio spans WAN, LAN, and enterprise security solutions. We are the global leader in Wireless-WAN enterprise connectivity and are rapidly expanding our Private 5G and Secure Access Service Edge (SASE) offerings.
What Will You Do:
• Lead the design and deployment of machine learning and statistical models that solve complex business challenges.
• Architect and build Agentic AI solutions including multi-agent systems, autonomous reasoning loops, tool-using agents, and orchestration frameworks for intelligent network operations.
• Design and implement LLM-powered applications using RAG, domain-adapted models, prompt engineering, structured outputs, and evaluation frameworks such as RAGAS and LLM-as-Judge.
• Partner with data engineers, product managers, and architects to identify high-value AI opportunities and deliver scalable production solutions.
• Perform data exploration, feature engineering, and dataset preparation for ML and LLM-based systems.
• Evaluate model performance and ensure robustness, interpretability, fairness, and reliability across traditional ML and transformer-based models.
• Develop reusable AI frameworks, tools, and libraries that improve team productivity and solution quality.
• Stay current with advancements in AI/ML research, open-source technologies, and industry trends.
What You Will Bring:
• 8–14 years of experience in Data Science and Machine Learning with increasing ownership and technical leadership.
• Strong expertise in regression, classification, clustering, ensemble methods, time-series analysis, and anomaly detection.
• Hands-on experience building LLM solutions using RAG, prompt engineering, vector databases, context management, and orchestration frameworks.
• Experience designing Agentic AI workflows, including tool-calling agents, memory architectures, and autonomous decision-making systems.
• Knowledge of model fine-tuning techniques including LoRA, QLoRA, PEFT, instruction tuning, and RLHF/DPO approaches.
• Strong programming skills in Python, proficiency in SQL, and familiarity with distributed data processing platforms.
• Solid software engineering practices with clean, testable, reproducible code and CI/CD awareness.
• Experience deploying, monitoring, and scaling AI models in production environments.
• Proven track record of leading AI/ML initiatives from problem definition through deployment and continuous improvement.
Preferred Qualifications:
• Master's or PhD in Computer Science, Statistics, Mathematics, or a related discipline.
• Experience with deep learning frameworks such as PyTorch and TensorFlow, including training or fine-tuning large models.
• Understanding of responsible AI practices, model safety, guardrails, hallucination mitigation, bias evaluation, and output filtering.
• Experience with function-calling and tool-use patterns in LLM applications and reliable agent design.
• Exposure to telecom, network operations, AIOps, or related domains is highly desirable.
• Contributions to open-source projects or published research are an advantage.
All academic credentials must be from recognized and accredited institutions and are subject to verification.