Technical Lead – AI & Data Science

Experience :
7+ Years

Required Skills :

• Master’s in Data Science, Machine Learning, or a related field.
• 7+ years of hands-on experience in applied AI/ML roles, including 3+ years in a technical leadership or architect capacity.
• Proven success leading projects involving causal modeling, LLMs, intelligent agents, or explainable AI in a telco or large-scale operations setting.
• Deep knowledge of telecom data and workflows, such as alarm management, OSS/BSS systems, SNMP traps, root cause correlation, or network analytics.
• Prior experience building production-grade AI services in a telco NOC or network engineering environment
• Strong programming and architectural skills (Python, SQL, ML frameworks, cloud-native architectures).
• Experience with modern AI agent stacks (LangChain, AutoGen, CrewAI, or custom-built orchestration layers).
• Familiarity with vector search, RAG pipelines, embeddings, and knowledge graphs in operational use cases.
• Excellent communication, mentorship, and stakeholder engagement skills.

Preferred:

• Familiarity with MLOps tools (e.g., MLflow, Vertex AI, Sagemaker) and deployment pipelines
• Contributions to AI/ML communities or open-source projects in causal reasoning or LLM applications

Responsibilities:

• Lead architecture and design of end-to-end AI solutions, including causal inference models, LLM agents, and NLP systems tailored for telco operations.
• Oversee the development and integration of AI agents capable of understanding, explaining, and resolving issues using telco alarm data, SNMP traps, logs, traces, and ticket history.
• Drive experimentation and modeling efforts using advanced techniques in causality, graph analytics, language modeling, and root cause reasoning.
• Collaborate closely with network engineers, product managers, and executive leadership to translate business challenges into scalable AI applications.
• Mentor a team of data scientists, promoting best practices in software engineering, experimentation, and model lifecycle management.
• Own delivery of AI components into production workflows in coordination with MLOps, cloud, and data engineering teams.
• Stay abreast of the latest advancements in LLMs, generative AI, and domain-specific AI for operations, incorporating relevant breakthroughs into team strategy.