Posted last month
AI Developer
@ J&M Group
Canada
SIGNALS
Overview
J&M Group is hiring a full-time AI Developer to design, build and deploy AI-powered applications, MLOps/LLMOps pipelines and data engineering frameworks. The role requires 10+ years of experience in data engineering, AI application development and cloud data platforms, and involves working with BFSI clients and sensitive financial data.
Responsibilities:
- Design and deploy AI applications using LLMs, RAG and agentic AI
- Implement MLOps and LLMOps with CI/CD, monitoring and rollback
- Build ETL/ELT and streaming data pipelines with quality validation
- Own production AI and data solutions after deployment
Requirements summary:
- 10+ years in data engineering and AI application development
- Strong Python, PySpark, SQL and FastAPI skills
- Hands-on experience with Claude, RAG and vector databases
- Experience with BFSI clients and sensitive financial data
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-10-08
- First seen by our source
- 2026-11-11
- Source record updated
- 2026-11-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Experience | 10+ years |
| Hours per week | 40 (estimated) |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
J&M Group
| Field | Value |
|---|---|
| Open jobs on Jobsy | 71 |
Job description
Required Experience
10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and production-grade software engineering.
Technical Skills
Strong 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.
10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and production-grade software engineering.
Technical Skills
Strong 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.
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