Senior Generative AI Engineer

Mississauga, Ontario, CanadaHybridFull TimeSeniorPosted 2 months ago

Is this role right for you?

Upload your resume and get a skill-by-skill breakdown — see exactly where you match, where you're close, and what to highlight. Not a mystery percentage.

Get a tailored resume highlighting what this role needs.

Role summary

We are seeking a Senior Generative AI Engineer with 8-10 years of experience in Software Engineering, AI/ML, or Systems Development. The role focuses on building and deploying production-grade Generative AI solutions, specifically LLM-powered enterprise applications. Key responsibilities include developing RAG pipelines, advanced prompt engineering, agentic workflows, and leveraging ML/AI libraries and vector databases. Proficiency in Python is mandatory. Experience with MLOps principles, CI/CD pipelines, and deploying models into production is critical. This is a hybrid role based in Mississauga, Canada.

Hiring: Senior Generative AI Engineer (LLM / RAG / MLOps)

Location: Mississauga, Canada (Hybrid)

About the Role

We are seeking Senior Generative AI Engineers with strong hands-on experience in building and deploying LLM-powered, enterprise-grade applications. This role requires deep expertise in RAG pipelines, prompt engineering, and production-level AI systems, not just foundational knowledge.

✅ Experience

8–10 years of experience in Software Engineering / AI/ML / Systems Development

Proven experience building production-grade GenAI solutions (Critical)

Generative AI & LLM Expertise (Critical)

Strong hands-on experience with LLMs (OpenAI, Gemini, Claude, Llama, Mistral, etc.)

Deep expertise in:

RAG pipelines (must-have, advanced level)

Prompt engineering, tuning, and prompt patterns

Agentic workflows and multi-step reasoning systems

Experience with evaluation frameworks, observability, and LLM performance tuning

Programming & Frameworks

Strong proficiency in Python (Mandatory)

Hands-on experience with:

LangChain, LlamaIndex (or equivalent)

ML/AI libraries: PyTorch, TensorFlow, Transformers

Data libraries: Pandas, NumPy, scikit-learn

API frameworks: FastAPI

Data & Retrieval

Strong experience with:

Vector databases (Pinecone, PGVector, MongoDB Atlas, Neo4j)

Retrieval strategies and hybrid search techniques

Ability to handle large-scale unstructured data pipelines

Deployment & MLOps (Critical)

Hands-on experience deploying LLM/GenAI models into production

Strong understanding of:

MLOps principles, model lifecycle, and monitoring

CI/CD pipelines (Jenkins, GitLab CI, Azure DevOps, ArgoCD)

Cloud & Infrastructure

Ready to apply?

You'll be redirected to Mamsys World's application page.

Is this role right for you?

Role summary

Similar roles