Senior AI Developer (GenAI / LLM)

Senior AI Developer (GenAI / LLM)

Location: Mississauga, Canada (Hybrid)

Role Overview

We are seeking experienced Senior AI Developers with strong expertise in Generative AI and Large Language Models (LLMs). The ideal candidate will have hands-on experience in building, deploying, and optimizing AI-powered applications, with a strong foundation in Python and modern AI/ML frameworks.

Key Responsibilities

AI/ML & GenAI Development

Design, develop, and deploy AI/ML and GenAI-based applications

Build and optimize LLM-powered solutions using models such as Gemini, OpenAI, Claude, Mistral, and Llama

Implement and enhance Retrieval-Augmented Generation (RAG) pipelines, including advanced techniques

Develop and fine-tune prompt engineering strategies and reusable prompt templates

Work with agentic frameworks for AI-driven use cases

Apply guardrails and evaluation frameworks to ensure performance, safety, and reliability

Programming & Data Engineering

Develop scalable solutions using Python (mandatory)

Utilize libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, LangChain, and LlamaIndex

Integrate GenAI capabilities with enterprise systems via APIs and orchestration tools

Work with vector databases (PG Vector, Pinecone, MongoDB Atlas, Neo4j)

Handle and process large volumes of unstructured data

Deployment & MLOps

Deploy LLM/GenAI models into production environments

Implement MLOps best practices, including model evaluation and monitoring

Build and manage CI/CD pipelines using tools like Jenkins, GitLab CI, Azure DevOps, or ArgoCD

Cloud & Infrastructure

Deploy and manage applications using Kubernetes/OpenShift

Work in cloud-native environments with scalable and containerized architectures

Required Qualifications

8–10 years of experience in application development or systems analysis

Strong foundation in Machine Learning, NLP, Neural Networks, and Data Science

Hands-on experience with LLMs and GenAI frameworks

Proven expertise in RAG pipeline implementation (critical requirement)

Strong proficiency in Python (mandatory)

Experience with LLM deployment and MLOps (critical)