Python /Pyspark Developer

Atlantis IT Group; Toronto; On-site; CAD 110,000 - 165,000; Full time

Job summary

Atlantis IT Group is seeking a seasoned Data & AI Engineer to design, build, and operate data platforms and AI-powered applications. You will work across ETL/ELT pipelines, data quality, and governance while leveraging PySpark, Python, SQL, and Cloud services.

You will implement MLOps practices, manage model lifecycles, and ensure secure, scalable production systems with observability and automated testing.

Qualifications

• Experience in building AI-powered applications and data pipelines.
• Hands-on with Python, PySpark, SQL and REST APIs.
• Knowledge of MLOps, CI/CD, and deployment automation.

Skills

Python

PySpark

SQL

REST APIs

FastAPI

AI Application Development

Data Engineering

MLOps

CI/CD

MLflow

Kubernetes

Airflow

Cloud Platforms

DataOps

Security & Governance

Pandas

Vector Databases

Semantic Search

RAG

Education

Bachelor's degree in Computer Science/Engineering/Information Systems/Data Management

Tools

Git

Docker

Job description

• Good years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and Production-grade Software Engineering.
• Strong hands-on expertise in Python, PySpark, SQL, REST APIs, FastAPI, and enterprise application integration.
• Experience designing and building AI-powered applications using Claude, Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases, Semantic Search, and Agentic AI patterns.
• Deep understanding of Data Engineering frameworks, including ETL/ELT, batch and streaming pipelines, data quality, metadata management, lineage, observability, and DataOps practices.
• Experience implementing and operationalizing MLOps / LLMOps capabilities including model lifecycle management, CI/CD pipelines, MLflow, monitoring, deployment automation, rollback, and production support.
• Strong knowledge of cloud and modern data platforms, including compute, storage, integration, orchestration, security, networking, monitoring, and cost optimization services.
• Hands-on experience with Git, Docker, Kubernetes, Airflow, MLflow, CI/CD, and distributed data processing frameworks.
• Strong understanding of enterprise security and governance principles, including IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.
• Bachelor's degree in computer science, Engineering, Information Systems, Data Management, or a related discipline.
• Preferred certifications in Cloud Platforms, Kubernetes, AI Engineering, MLOps, Data Engineering, Enterprise Architecture, Security, or Program Delivery
• Strong hands-on data discovery skills, including profiling unfamiliar client data with Python, pandas, PySpark, and SQL; assessing coverage and quality; and quantifying volumes, distributions, edge cases, and business-process gaps before development.
• Ability to rapidly build thin working solutions using real client data and APIs, validate them with users, and iteratively harden them with automated tests, security controls, error handling, observability, and production support procedures.
• Experience creating business-relevant evaluation datasets with client SMEs, measuring ML or LLM quality, performing structured error analysis, grouping failures by root cause, and prioritizing improvements across releases.
• Ability to measure user adoption and business impact after launch using usage data and agreed KPIs such as time saved, error reduction, straight-through processing, quality improvement, or productivity gains