Infosys is hiring AI Technical Lead
Required Qualifications:
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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.
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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.
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At least 4 years of Information Technology experience.
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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
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8+ years of overall technology experience with 5+ years in Contact Center and Conversational AI architecture.
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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.
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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.
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Advanced AI Paradigms: Continuously integrate cutting-edge methodologies (structured reasoning, tool-use optimization, memory systems, multi-agent collaboration).
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Technical Mentorship: Coach developers on AI security, token-economics, UX patterns, and safe deployment practices. Establish coding standards and architectural guardrails
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Proven leadership delivering production-grade ML/AI systems.
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Strong foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science.
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Knowledge of Google’s GECX is a strong bonus.
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Expertise in systems-level programming, distributed architecture, and performance-critical code.
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Systems Architecture: Expertise designing distributed AI systems, multi-agent frameworks, and scalable RAG pipelines.
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LLM Internals: Deep understanding of transformer mechanics, attention patterns, tokenization, and inference optimization.
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Evaluation Science: Ability to design adversarial tests, regression suites, and domain-specific scoring functions.
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AI Security: Knowledge of jailbreak prevention, prompt-injection defense, secure tool-use, and zero-trust agent routing.
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Tokenomics: Ability to optimize token usage, context windows, and cost-latency trade-offs.
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Distributed Systems: Experience with load balancing, sharding, concurrency, and high-availability inference clusters. Observability: Familiarity with tracing, logging, telemetry, and agent-level debugging.
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DevOps for AI: CI/CD for model updates, containerization, GPU orchestration, and rollout strategies.
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Mentorship & Leadership: Ability to enforce engineering discipline, code quality, and architectural guardrails.
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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.
