Required Experience 10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and production-grade software engineering.

Technical Skills

Solid proficiency in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Apache Airflow, and MLflow.

Hands-on experience in developing AI-powered applications using Claude, prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, semantic search, and agentic AI architectures.

Expertise in implementing MLOps and LLMOps practices, including model lifecycle management, CI/CD pipelines, performance monitoring, and rollback mechanisms.

Key Responsibilities

AI Application Development: Design, develop, and deploy scalable AI-powered applications leveraging large language models (LLMs), RAG architectures, vector databases, semantic search, and agentic AI patterns.

MLOps and LLMOps: Implement and manage end-to-end MLOps and LLMOps capabilities, including model lifecycle management, automated CI/CD pipelines, experiment tracking, monitoring, and rollback strategies using tools such as MLflow.

Data Engineering: Design, build, and maintain robust end-to-end data engineering frameworks, including ETL/ELT processes, batch and streaming pipelines, data integration, data quality validation, and DataOps practices.

API Integration and Prototyping: Rapidly develop and validate prototypes using real-world client data, external APIs,



and enterprise systems to demonstrate technical feasibility and business value.

Model and Application Evaluation: Evaluate and measure the quality, accuracy, reliability, and performance of ML and LLM-based solutions, implementing appropriate testing and validation frameworks.

Production Deployment and Support: Take end-to-end ownership of AI and data solutions following deployment, ensuring production readiness, operational stability, performance optimization, monitoring, and ongoing maintenance.

Cloud Data Platforms and Engineering: Develop and operate scalable, reliable data and AI solutions across cloud-based environments, following software engineering best practices and established architectural standards.

Security and Compliance: Work within Banking, Financial Services, and Insurance (BFSI) data environments, ensuring compliance with security and regulatory requirements, including identity and access management (IAM), role-based access control (RBAC), and data encryption.

Preferred Domain Experience Experience working with BFSI clients, enterprise data platforms, and sensitive financial data.

Strong understanding of production-grade AI systems, secure data processing, and enterprise application deployment.

Proven ability to translate business requirements into scalable, reliable, and production-ready AI and data engineering solutions.

📌 AI Developer (Canada)
🏢 Ju0026M Group
📍 Canada