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Senior AI Developer (RAG, Agents & Microservices) | Onsite | Salary in Euros | Ontario Canada
- Compensation
- Not disclosed
- Location
- Ontario, Canada
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Software Engineering: Strong core software engineering fundamentals with advanced-intermediate to expert-level Python skills (including asyncio, concurrency patterns, and performance tuning).
- AI Architecture: Hands-on experience building transparent AI systems using standard Python libraries, HTTP clients (httpx, requests), and minimal-framework patterns rather than relying solely on high-
- API & Backend: Proven track record of developing production REST APIs and microservices using FastAPI.
- Structured Outputs: Deep experience with Pydantic validation, JSON schema design, and structured extraction techniques.
- Ecosystem Knowledge: Familiarity with Google Enterprise Conversational Search/Extension tools (GECX).
- Evaluation & Operations: Experience with prompt-level debugging, evaluation frameworks, and instrumenting tracing/observability for agentic workflows.
Full description
Job Title: Lead / Senior AI Developer (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 an experienced AI Developer to build, optimize, and maintain production-grade GenAI systems. In this role, you will design native RAG pipelines, architect agentic workflows, and optimize context engineering for high-reasoning output. We emphasize transparent, minimal-framework AI development—focusing on raw API integrations, HTTP clients, and robust Python microservices built with FastAPI over heavy abstraction layers.
Key Responsibilities
- Build Native RAG Pipelines: Architect and implement custom chunking strategies, embeddings, hybrid search, re-ranking mechanisms, and domain-specific retrieval logic.
- Agentic Orchestration: Design and deploy multi-step AI agents featuring working memory, function calling/tool execution, state tracking, and deterministic control flows.
- Context Engineering & Optimization: Optimize prompts, context packing, and token economics to balance high reasoning quality with minimal latency and inference costs.
- Microservice Development: Build, deploy, and maintain transparent, highly debuggable AI microservices using FastAPI.
- Structured Data Extraction: Implement deterministic extraction pipelines using Pydantic models, custom JSON schemas, and advanced structured-output prompting.
- Testing, Observability & Debugging: Instrument end-to-end tracing, logging, token usage metrics, and latency monitoring. Write robust unit tests for agents and regression test suites for RAG components.
Required Qualifications
- Software Engineering: Strong core software engineering fundamentals with advanced-intermediate to expert-level Python skills (including asyncio, concurrency patterns, and performance tuning).
- AI Architecture: Hands-on experience building transparent AI systems using standard Python libraries, HTTP clients (httpx, requests), and minimal-framework patterns rather than relying solely on high-level abstractions.
- API & Backend: Proven track record of developing production REST APIs and microservices using FastAPI.
- Structured Outputs: Deep experience with Pydantic validation, JSON schema design, and structured extraction techniques.
- Ecosystem Knowledge: Familiarity with Google Enterprise Conversational Search/Extension tools ().