Senior Software Engineer - AI
New
Canada, EST overlap hoursFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PostgreSQLPythonFastAPICI/CDLLM
Requirements
- 5+ years of experience developing production-grade backend APIs with Python.
- At least 2 years of hands-on experience with FastAPI or similar asynchronous Python frameworks.
- Strong expertise in asynchronous programming, dependency injection, Pydantic, and backend observability practices.
- Practical experience with AI agent frameworks such as Semantic Kernel or equivalent technologies.
- Proven experience building RAG pipelines using vector search technologies such as Azure AI Search, pgvector, or Chroma.
- Solid knowledge of PostgreSQL, SQLModel, SQLAlchemy, and database migration tools such as Alembic.
- Experience integrating LLM APIs including OpenAI and Gemini, with an understanding of structured outputs and AI workflows.
- Familiarity with dependency management tools, containerized environments, and CI/CD ownership from development to deployment.
- Excellent analytical thinking, troubleshooting, and problem-solving skills.
- Comfortable working in a fully remote environment with regular collaboration during EST overlap hours.
Responsibilities
- Design, develop, and scale asynchronous REST and WebSocket APIs using Python, FastAPI, and modern backend architecture principles.
- Build and optimize multi-agent AI workflows capable of orchestrating specialized LLM agents across different execution patterns.
- Integrate multiple LLM providers through flexible, provider-agnostic systems that support performance optimization and cost-aware routing.
- Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and search technologies to improve AI response quality and contextual relevance.
- Implement AI-powered tools and assistants capable of advanced data analysis and file-processing functionalities.
- Manage database schemas, migrations, and high-concurrency PostgreSQL environments while ensuring performance and reliability.
- Maintain and improve CI/CD pipelines, automated testing, containerization, and deployment workflows.
- Monitor application performance through observability tools, structured logging, tracing, and latency optimization.
- Promote engineering best practices and contribute to a collaborative culture focused on innovation and AI-assisted development.
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