Senior Generative AI Engineer (Python, LLM, RAG)
Location: Mississauga, Canada (Hybrid)
Full-Time
Role Overview:
We are hiring experienced Generative AI Engineers to design and build enterprise-grade AI solutions within the banking domain. This role focuses on developing scalable LLM-powered applications, implementing advanced RAG pipelines, and deploying production-ready AI systems.
The ideal candidate will have strong Python expertise, hands-on experience with LLMs and GenAI frameworks, and a deep understanding of end-to-end AI/ML lifecycle and MLOps practices.
Key Responsibilities:
- Design and develop Generative AI applications using Large Language Models (LLMs)
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines with advanced techniques
- Develop scalable APIs and microservices using Python (FastAPI, Flask, etc.)
- Implement prompt engineering strategies, prompt tuning, and reusable templates
- Integrate LLM solutions with enterprise systems via APIs, knowledge graphs, and orchestration frameworks
- Work with vector databases (Pinecone, PGVector, Mongo Atlas, Neo4j) for semantic search and retrieval
- Handle and process large-scale unstructured datasets
- Deploy AI/ML models into production with strong MLOps practices
- Build and maintain CI/CD pipelines for AI solutions
- Ensure model performance, reliability, and safety using guardrails and evaluation frameworks
- Collaborate with cross-functional teams to deliver high-impact AI solutions
Required Skills & Qualifications:
Experience:
- 6–10 years in application development, AI/ML, or systems engineering
Core AI/ML Expertise:
- Strong foundation in:
- Machine Learning & Data Science
- Natural Language Processing (NLP)
- Neural Networks & LLMs
- Statistics
Generative AI & LLMs:
- Hands-on experience with:
- OpenAI, Google Gemini, Anthropic Claude, Mistral, LLaMA
- Strong experience with:
- RAG pipelines (must-have)
- Prompt engineering & tuning
- Agentic frameworks (LangChain, LlamaIndex, etc.)
- Guardrails & GenAI evaluation techniques
Programming & Tools:
- Strong proficiency in Python
- Experience with:
- Pandas, NumPy, scikit-learn
- PyTorch / TensorFlow
- Transformers, Hugging Face
- FastAPI
- LangChain, LlamaIndex
Data & Infrastructure:
- Experience with:
- Vector databases: Pinecone, PGVector, MongoDB Atlas, Neo4j
- Handling large-scale unstructured data
Deployment & MLOps:
- Experience deploying AI models to production
- Strong understanding of:
- MLOps, model evaluation
- CI/CD tools: Jenkins, GitLab CI, Azure DevOps, ArgoCD
Cloud & Containerization:
- Hands-on experience with:
- Kubernetes / OpenShift
- Cloud platforms (GCP, Azure, AWS preferred)
Soft Skills:
- Strong analytical and problem-solving skills
- Ability to work independently in complex environments
- Excellent communication and collaboration skills