Senior AI Architect

Future Connections

2d•Hybrid

About The Position

We are looking for a hands-on Agentic AI / ML Architect to design and build AI agent-based solutions for our projects and our clients. This is not a purely advisory role: you will define the architecture and also be part of the team that builds it, from the first prototype to production. You will work side by side with our engineering and project teams, who bring deep knowledge of the telecom domain, and you will be the reference person on AI. You will also take part in conversations with clients, understanding their needs and explaining your technical decisions in a clear way.

Requirements

6+ years of experience in data engineering, ML or AI engineering, with a hands-on profile
Strong knowledge of machine learning and solid experience designing and deploying AI/ML systems
Experience working with large data volumes, ideally in streaming or real-time environments
Recent experience (last 1–2 years) building and taking to production solutions with LLMs, RAG or AI agents
Good understanding of agentic concepts: LLM-based reasoning and planning, tool-using agents and multi-agent coordination
Good coding skills (Python) and familiarity with cloud-native architectures and AI/ML platforms
Good communication skills, comfortable talking to clients and explaining technical topics to non-technical people

Nice To Haves

Experience in regulated environments (banking, insurance, energy...) and AI governance or risk management
Knowledge of LLMOps / MLOps practices
Experience with AIOps platforms
Knowledge of the telecom world (OSS, 5G, O-RAN...)
Experience in consulting, pre-sales or RFPs
Certifications or training in cloud architecture or AI/ML

Responsibilities

Design and implement solutions based on LLMs and AI agents, from architecture to working code
Build RAG-based knowledge systems using internal documentation, procedures and operational data
Design end-to-end architectures, from data ingestion to automated actions
Define agent responsibilities, boundaries, autonomy levels and interaction models (single vs. multi-agent)
Implement safe execution mechanisms: guardrails, approvals, policy enforcement and rollback
Work with streaming and real-time data pipelines at high volumes
Set up how we evaluate, monitor and improve AI solutions in production
Take part in client meetings and technical workshops, and help assess and prioritize use cases
Share knowledge and good practices with the team while working on the same projects