Job Description

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.
Bachelor's 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.
Strong foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science.
Knowledge of Google's 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.