Job Description

Job Title: Lead / AI Technical Lead (RAG, Agents & Microservices)

Location: Canada

About client:

A global IT and consulting company based in Bangalore, India, providing IT services to clients worldwide across multiple industries.

Position Overview

We are seeking a hands-on AI Technical Lead to drive the architectural vision, evaluation frameworks, and system execution for high-scale GenAI systems. In this role, you will lead the design of distributed inference setups, multi-agent frameworks, and advanced RAG architectures. You will balance deep technical execution—optimizing attention patterns, token economics, and AI security—with technical mentorship, setting engineering guardrails for production-grade AI systems.

Key Responsibilities

• Architectural Leadership: Define and evolve end-to-end architectures for scalable RAG pipelines, stateful agent frameworks, and distributed inference environments prioritizing deterministic outputs, low latency, and high availability.
• Evaluation & Testing: Build custom evaluation harnesses, domain-specific scoring functions, and adversarial testing suites to benchmark agent reasoning, RAG accuracy, and model safety beyond off-the-shelf metrics.
• Advanced AI Implementation: Incorporate modern AI paradigms into production, including structured reasoning, tool-use optimization, persistent memory systems, and multi-agent collaboration protocols.
• AI Security & Governance: Enforce prompt-injection defenses, zero-trust agent routing, jailbreak prevention, and strict content-safety policies for auditability in regulated environments.
• Cost & Performance Optimization: Direct token-economics strategies—optimizing context-packing, token usage, and hardware constraints to manage cost-latency trade-offs.
• Mentorship & Standards: Establish engineering standards, code review protocols, and architectural guardrails across teams while coaching engineers on safe deployment and UX-aligned AI patterns.

Required Qualifications

• Proven AI Leadership: Demonstrated track record of leading technical teams and successfully architecting and shipping production-grade ML/AI systems.
• AI Foundations: Deep technical understanding of transformer internals (attention mechanisms, tokenization), vector mathematics, embedding spaces, agent orchestrations, and evaluation science.
• Distributed Systems Experience: Strong background in systems-level programming, concurrent systems, sharding, load balancing, and GPU orchestration.
• Ecosystem Expertise: Hands-on knowledge of Google Gemini Enterprise for Customer Experience (GECX) solutions or similar enterprise AI agent suites is highly desirable.

Core Competencies

DomainKey Focus Areas

System ArchitectureDistributed AI Systems, Multi-Agent Frameworks, High-Availability Inference Clusters

LLM InternalsTransformer Architecture, Attention Patterns, Tokenization, Inference Optimization

Evaluation & SecurityCustom Evaluation Harnesses, Adversarial Testing, Prompt-Injection Defense, Zero-Trust Routing

LLMOps & InfraGPU Orchestration, CI/CD for AI Models, Tracing, Telemetry, Containerization

Governance & QualityTokenomics Optimization, Content-Safety Guardrails, Technical Mentorship, Code Quality Standards

Experience: 8 + Years

Location: Canada

Work Mode: Onsite

Salary Range: Based on Experience

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