AI Engineer

Altitude Technology Solutions Inc; Toronto; On-site; CAD 120,000 - 190,000; Full time

Job summary

Altitude Technology Solutions Inc. is seeking an experienced AI systems engineer to design and implement agentic AI systems with tool-calling, retrieval, and secure action execution.

You will productionize LLMs, build evaluation frameworks, and develop robust pipelines for high reliability in production environments. You will connect agents to observability, incident management, and deployment systems, driving auditable outcomes and measurable business impact while maintaining deterministic

Qualifications

• 5+ years of software development in one or more languages (Python, C/C++, Go, Java).
• 3+ years designing, architecting, testing, and launching production ML systems.
• Practical experience with LLMs: API integration, prompt engineering, and tool-using agents.
• Knowledge of different LLMs, commercial and open source, and their capabilities.
• Solid grasp of statistics, ML concepts, and data structures.
• Strong analytical problem-solving, ownership, and cross-team collaboration.
• Cloud proficiency (ideally AWS) and container/orchestration experience.

Responsibilities

• Design and implement agentic AI systems with tool-calling and governance.
• Productionize LLMs: build retrieval pipelines, evaluation, and validation.
• Integrate agents with observability, incident management, and deployment systems.
• Collaborate with production engineers to translate pain points into AI roadmaps.
• Build safety rails, deterministic fallbacks, and rollback strategies.
• Optimize cost and latency with batching, caching, and routing strategies.
• Develop a RAG pipeline with data quality validation and knowledge freshness.
• Drive design reviews, experiments, and high-quality engineering practices.

Skills

Python

C/C++

Go

Java

ML systems design

Communication

Problem solving

Tools

AWS

ECS/EKS

SageMaker

Terraform

Job description

Exp- 10+ years

Must be able to Share Both Side of Driving License Copy

Must have good communication skills and should be confident

Job Description:

• Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
• Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
• Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
• Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
• Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
• Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
• Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
• Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns

ESSENTIAL SKILLS

• 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
• 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
• Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
• Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
• Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
• Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
• Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).