Python Developer

Position: Python /Pyspark Developer

Location: Toronto, ON (2-4 days hybrid)

Work Type: Full time

Requirement:

10+ 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