Infosys is hiring AI Technical Lead

Required Qualifications:

• Candidate must be located within commuting distance of Calgary, Canada or be willing to relocate to the area. This position may require travel in Canada.
• Bachelors degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
• At least 4 years of Information Technology experience.
• Candidates authorized to work for any employer in the Canada without employer-based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role at this time.

Required Skills

• 8+ years of overall technology experience with 5+ years in Contact Center and Conversational AI architecture.
• Architectural Leadership: Define and evolve the end-to-end architecture for RAG pipelines, agent frameworks, and distributed inference systems. Prioritize scalability, latency, and UX-aligned output determinism.
• Rigorous Evaluation: Build custom evaluation harnesses for AI agents, RAG, and LLM reasoning. Move beyond out-of-the-box metrics with domain-specific scoring, adversarial tests, and regression suites.
• Advanced AI Paradigms: Continuously integrate cutting-edge methodologies (structured reasoning, tool-use optimization, memory systems, multi-agent collaboration).
• Technical Mentorship: Coach developers on AI security, token-economics, UX patterns, and safe deployment practices.
  
  
  
  Establish coding standards and architectural guardrails
• Proven leadership delivering production-grade ML/AI systems.
• Robust foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science.
• Knowledge of Googles GECX is a strong bonus.
• Expertise in systems-level programming, distributed architecture, and performance-critical code.
• Systems Architecture: Expertise designing distributed AI systems, multi-agent frameworks, and scalable RAG pipelines.
• LLM Internals: Deep understanding of transformer mechanics, attention patterns, tokenization, and inference optimization.
• Evaluation Science: Ability to design adversarial tests, regression suites, and domain-specific scoring functions.
• AI Security: Knowledge of jailbreak prevention, prompt-injection defense, secure tool-use, and zero-trust agent routing.
• Tokenomics: Ability to optimize token usage, context windows, and cost-latency trade-offs.
• Distributed Systems: Experience with load balancing, sharding, concurrency, and high-availability inference clusters. Observability: Familiarity with tracing, logging, telemetry, and agent-level debugging.
• DevOps for AI: CI/CD for model updates, containerization, GPU orchestration, and rollout strategies.
• Mentorship & Leadership: Ability to enforce engineering discipline, code quality, and architectural guardrails.
• Responsible AI & Governance: Guardrail design, content-safety policy, and audit/traceability for regulated deployments.

The job may also entail sitting as well as working at a computer for extended periods of time. Candidates should be able to effectively communicate by telephone, email, and face to face.