Senior Data AI Engineer (GenAI & LLM)

Position: Senior Data AI Engineer (GenAI & LLM Data Platform)

Location: Onsite- Mississauga, ON

Job type: Full-Time/Permanent hiring

We are seeking an experienced Senior Data AI Engineer to design, construct, and optimize the production data pipelines and architectures required to drive our enterprise Machine Learning and Generative AI initiatives. In this role, you will be the bridge between complex data ecosystems and advanced AI applications. You will not only work with Large Language Models (LLMs) and ML frameworks but will also build the scalable, secure, and automated real-time data ingestion pipelines that feed them - ensuring institutional data governance in a regulated environment.

Core Responsibilities

• AI-Driven Data Pipeline Engineering: Design, build, and maintain scalable batch and real-time data ingestion pipelines using Python, SQL, and modern big data frameworks to feed ML models and LLM infrastructures.
• Vector Architecture & RAG Engineering: Architect enterprise-scale Knowledge Retrieval systems using Retrieval-Augmented Generation (RAG), structured data indexing, chunking strategies, and high-performance vector databases.
• Model Deployment & Feature Engineering: Build and optimize feature stores, scale data processing for model fine-tuning (PyTorch, TensorFlow, Scikit-learn), and expose model capabilities securely via microservices and REST APIs.
• Enterprise Collaboration: Partner with Data Architects, ML Engineers, Security teams, and financial business users to translate analytical requirements into highly reliable, production-grade automated pipelines.
• Data Security & AI Governance: Enforce strict data privacy, enterprise tokenization, PII masking, and data lineage standards to maintain complete compliance within regulated financial service frameworks.

Required Skillsets

• Experience: 6–10 years of strong hands-on development experience combining traditional Data Engineering with AI/ML systems implementation.
• Programming & Querying: Elite, production-grade proficiency in Python and advanced SQL.
• AI & LLM Data Foundations: Practical experience managing data prep for LLMs, prompt parsing, embedding generation, and working with vector search workflows.
• ML Foundations & Validation: Solid working knowledge of model development libraries (Scikit-learn, PyTorch, or TensorFlow) and ML telemetry evaluation metrics (F1 Score, Precision/Recall, RMSE).
• Data Infrastructure: Proven track record handling mass data manipulation, text processing, and ETL/ELT pipelines.

Preferred / Good to Have Skills

• Strong domain experience working with financial, banking, or transactional data structures.
• Hands-on engineering with dedicated Vector Databases (e.g., Pinecone, Milvus, Chroma, pgvector, or Qdrant).
• Experience with cloud data platforms and AI ecosystems (e.g., AWS, Azure, Databricks, Snowflake, or Google Vertex AI).