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Senior AI Developers (GenAI / LLM)

Mamsys World
Mississauga, Ontario, Canada
Full-time
AI tools:
Gemini
OpenAI
Claude
Mistral
Llama
PyTorch
TensorFlow
LangChain
LlamaIndex
Pinecone
Vertex AI
Hugging Face
Applications go directly to the hiring team

Full Description

Senior AI Developers (GenAI / LLM)

Location: Mississauga, Canada (Hybrid)

We are actively looking for skilled Senior AI Developers to join a high-impact Service AI FPC Scrum team. If you’re passionate about Generative AI, LLMs, and building scalable AI solutions, this is a great opportunity to work on cutting-edge initiatives.

Role Overview

We are seeking experienced professionals with 8–10 years of experience in application development or systems analysis, with strong expertise in AI/ML and Generative AI technologies.

Key Responsibilities

Design, build, and deploy LLM-based applications

Develop and implement RAG (Retrieval-Augmented Generation) pipelines

Work with prompt engineering, tuning, and reusable prompt frameworks

Build agentic AI solutions and ensure model safety using guardrails

Integrate AI solutions with enterprise systems using APIs and orchestration tools

Deploy models in production with strong MLOps & CI/CD practices

Work with large-scale unstructured data pipelines

Required Skills

Primary Skill: Python + LLM (Preferred)

Alternate: Java + LLM (must be willing to work on Python)

Core Expertise:

Generative AI, Machine Learning, NLP, Neural Networks

Hands-on with LLMs: Gemini, OpenAI, Claude, Mistral, Llama

Strong experience in RAG implementations (must-have)

Prompt Engineering & Agentic Frameworks

Tech Stack:

Python (Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers)

FastAPI, LangChain, LlamaIndex

Vector DBs: Pinecone, PGVector, MongoDB Atlas, Neo4j

Cloud/AI Platforms: Vertex AI, Hugging Face

Deployment & DevOps:

MLOps, model evaluation & production deployment

CI/CD tools: Jenkins, GitLab CI, Azure DevOps, ArgoCD

Kubernetes / OpenShift for containerized deployments

Applications go to the hiring team directly