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AI Technical Lead – Calgary

Infosys · Calgary

Senior 🇬🇧 English
Contact Center and Conversational AI architecture RAG pipelines Agent frameworks Distributed inference AI security Vector math Embeddings LLM internals Load balancing Sharding Concurrency

Description du poste

About the role

Infosys is seeking an AI Technical Lead to drive the design and delivery of cutting‑edge Conversational AI solutions for contact‑center environments. Based in Calgary, you will own the end‑to‑end architecture of Retrieval‑Augmented Generation (RAG) pipelines, agent frameworks, and distributed inference systems, ensuring they meet scalability, latency, and user‑experience goals.

Key responsibilities

  • Define and evolve the architecture for RAG pipelines, multi‑agent frameworks, and distributed inference.
  • Build custom evaluation harnesses for AI agents, incorporating domain‑specific scoring, adversarial testing, and regression suites.
  • Integrate advanced AI paradigms such as structured reasoning, tool‑use optimization, memory systems, and multi‑agent collaboration.
  • Provide technical mentorship, establishing coding standards, security guardrails, and best practices for AI deployment.
  • Lead production‑grade ML/AI projects from prototype to fully operational services.

Required profile

  • Located within commuting distance of Calgary or willing to relocate.
  • Bachelor’s degree (or equivalent) from an accredited institution.
  • At least 4 years of IT experience and authorization to work in Canada without sponsorship.
  • 8 + years of overall technology experience, including 5 + years in Contact Center and Conversational AI architecture.
  • Proven leadership delivering production‑grade AI/ML systems.

Required skills

  • Contact Center and Conversational AI architecture
  • RAG pipelines and agent frameworks
  • Distributed inference and scalable AI system design
  • AI evaluation science (adversarial tests, regression suites)
  • AI security (jailbreak prevention, prompt‑injection defense)
  • Token‑economics and cost‑latency optimization
  • Vector math, embeddings, LLM internals
  • Systems‑level programming and performance‑critical code
  • Distributed systems concepts (load balancing, sharding, concurrency)
  • Knowledge of Google GECX (strong bonus)

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Infosys

Calgary