Required Skills & Experience
- 10+ years of experience in AI, machine learning, data science, data engineering, or solution architecture roles.
- Strong expertise in Python, data pipeline design, and large-scale data processing.
- Experience designing and implementing AI/ML solutions, predictive models, and intelligent automation capabilities.
- Hands-on experience with LLMs, AI agents, RAG architectures, and agent orchestration frameworks.
- Deep understanding of telemetry, observability, monitoring, and operational data ecosystems.
- Experience building scalable data ingestion, enrichment, and analytics pipelines.
- Ability to translate business and operational challenges into AI-driven solutions and technical architectures.
- Experience creating detailed solution designs and guiding offshore engineering teams through implementation.
- Strong communication skills with experience partnering directly with business, engineering, and operations stakeholders.
Job Description
Overview
We are seeking a highly strategic AI Architect / Principal Data Scientist to lead the design of intelligent automation capabilities supporting network provisioning, network health analysis, and operational decision-making. This individual will work directly with business and technology stakeholders to define AI-powered solutions that leverage existing logging, telemetry, monitoring, and operational data sources.
The role will focus on architecture, data strategy, agent design, and AI solution development, while providing implementation guidance to offshore engineering teams.
Key Responsibilities
- Design enterprise AI architectures supporting network automation, observability, and operational intelligence.
- Define data ingestion, processing, enrichment, and analytics pipelines utilizing existing telemetry and logging platforms.
- Architect AI and machine learning solutions that improve network health monitoring, anomaly detection, provisioning efficiency, and operational decision-making.
- Design agentic workflows and autonomous agents capable of analyzing operational data and recommending or initiating actions.
- Develop data models and AI governance frameworks supporting reliable and scalable AI implementations.
- Collaborate with network engineering, software engineering, and operations teams to identify high-value AI use cases.
- Produce detailed technical designs and implementation guidance for offshore engineering teams.
- Establish standards for model evaluation, monitoring, explainability, and performance measurement.
- Drive adoption of modern AI, machine learning, and generative AI technologies across the organization.