Role overview
Trinetix is looking for a skilled AI Engineer.
Join our team at Trinetix and collaborate on a project with a global telecommunications leader operating one of the world’s most reliable Internet backbones across more than 100 countries. In this role, you will contribute to large-scale AI initiatives focused on improving network operations through intelligent automation, predictive insights, and AI-driven workflows.
You will work on GenAI applications, AIOps intelligence, and RAG-based systems, helping to transform how complex telecom infrastructure is monitored, analyzed, and operated. The role combines engineering, data, and product thinking to deliver AI solutions with measurable business impact.
What you'll work on
- Design and implement AI/ML pipelines, including LLM-based applications, GenAI frameworks, and vector retrieval systems.
- Build production-grade AI integrations in Python, ensuring scalability and reliability.
- Apply enterprise AI patterns such as RAG, observability, agent workflows, and model evaluation.
- Architect secure, scalable integrations between cloud platforms and enterprise systems.
- Develop data pipelines, event streams, and observability frameworks to support AI at scale.
- Collaborate with Data Scientists on feature engineering, model evaluation, and experiment design.
- Contribute to AI-driven telecom solutions by learning and applying concepts in IP routing, EVPN/L3VPN, telemetry, and optical networking.
- Translate technical capabilities into measurable business impact through KPIs, ROI analysis, and product-focused thinking.
What we're looking for
- 5+ years of experience in data, analytics, machine learning, or AI-driven environments.
- Strong hands-on expertise with LLMs, GenAI frameworks, ML/analytics pipelines, and vector databases.
- Proficiency in Python and experience delivering production-grade AI solutions.
- Solid understanding of enterprise AI patterns (RAG, observability, agent workflows, model evaluation).
- Knowledge of data pipelines, event-driven architectures, and system-to-system integration.
- Familiarity with data science practices: feature engineering, statistical modeling, experiment design, and hypothesis-driven analysis.
- Ability to work with structured and unstructured data, conduct exploratory analysis, and guide AI solution design.
- Experience collaborating across engineering, architecture, IT, and operations teams.
- Strong communication skills to simplify AI concepts for non-experts.
- Curiosity and adaptability to learn emerging AI tools and telecom domain concepts.
Tags & focus areas
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