Job Closed
This listing is no longer active.
Senior AI Engineer
Location
Canada
Posted
1 day ago
Salary
0
Seniority
Senior
Job Description
Senior AI Engineer
Inabia Solutions and Consulting, Inc.
• Develop and experiment with LLMs • Develop REST APIs using Python and FastAPI • Work with AI-assisted and agentic coding tools • Build, deploy, and secure MCP servers • Understand and apply multi-agent systems for problem-solving • Design RAG systems including vector databases and semantic search • Write prompts for various use cases • Develop generative solutions and integrate them into production • Optimize backend systems and implement security best practices • Evaluate LLM systems and ensure quality metrics are met
Job Requirements
- 8 – 10 Years of experience
- 2+ years of experience developing and experimenting with LLMs
- 8+ years of experience developing APIs with Python
- Hands-on, daily use of AI-assisted and agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot, autonomous coding agents)
- Strong experience with Python, particularly in building REST APIs using frameworks like FastAPI
- Grounding in NLP and machine learning as they relate to building LLM systems
- Strong experience working with key LLM models APIs (e.g., OpenAI, Anthropic)
- Experience building, deploying, and securing MCP servers at scale
- Understanding of multi-agent systems and their applications in complex problem-solving scenarios
- Designing and implementing RAG systems end to end: vector databases, semantic search, retrieval quality, and chunking strategy
- Experience with prompt writing for various use cases
- Experience with generative solutions released to prod, at scale, beyond POCs
- Proficiency with server-side events, event-driven architectures, and messaging systems
- Strong critical thinking and systems thinking skills, with experience debugging, optimizing, and making sound engineering decisions across complex backend systems
- Solid understanding of security best practices for backend systems, including authentication and data protection
- Experience developing AI/ML technologies within large and business critical applications
- Building evaluation into LLM systems: eval harnesses, regression suites, LLM-as-judge, and offline/online quality metrics
Benefits
- 100% Remote USA / Canada
- Contract + Possibility of an extension
- Mode of Interviews – Virtual
Related Guides
Related Job Pages
More AI Engineer Jobs
• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.
• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.
• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students.
• Design and build AI-enabled features for various education products, from assessments to tutoring. • Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints. • Build evaluation approaches that measure output quality, consistency, usefulness and failure modes. • Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate. • Integrate LLM APIs, AI services and education data into reliable product capabilities. • Design practical human-review, control and escalation workflows for AI-supported decisions. • Improve the reliability, latency, cost and quality of AI services in production. • Build prototypes quickly, test them against real education use cases and productionise successful approaches. • Develop suitable guardrails, monitoring and auditability for AI outputs. • Work with Backend Engineers on service architecture, data handling, integrations and scalability. • Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students. • Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards. • Write clean, tested and maintainable code.
