Machine Learning Engineer / MLOps Engineer

About the role

We CGI is seeking a highly motivated Machine Learning Engineer / MLOps Engineer to design, develop, deploy, and maintain scalable machine learning solutions in a cloud native environment. The ideal candidate will have hands-on experience across the machine learning lifecycle, including model development, deployment, monitoring, and operationalization using AWS cloud services and modern MLOps practices.

Responsibilities

• Design, build, and maintain end-to-end machine learning pipelines and MLOps workflows.
• Develop, train, evaluate, and optimize machine learning models using Python and industry-standard ML libraries.
• Implement model lifecycle management using MLflow, including experiment tracking, model registration, versioning, and deployment.
• Automate model deployment processes using CI/CD pipelines and GitHub Actions.
• Monitor deployed models for performance, drift, reliability, and operational health.
• Define and implement model performance metrics, monitoring dashboards, and alerting mechanisms.
• Develop and maintain RESTful APIs and backend services using FastAPI.
• Design scalable database schemas and data access layers using PostgreSQL and SQLAlchemy ORM.
• Deploy and manage containerized applications using Amazon ECS and Amazon ECR.
• Configure and manage cloud native services including Amazon API Gateway, Application Load Balancer (ALB), Amazon RDS, and Amazon S3.
• Collaborate with cross-functional teams to ensure secure, scalable, and maintainable AI/ML solutions.
• Participate in code reviews, architecture discussions, and continuous improvement initiatives.
• Troubleshoot production issues and optimize application and infrastructure performance.
• Contribute to AI/ML platform enhancements and adoption of best practices across engineering teams.

Requirements

• At least 3+ years of hands-on experience in Machine Learning Engineering or MLOps.
• Strong experience with: MLflow for experiment tracking and model lifecycle management.
• Spark ML and distributed machine learning workflows.
• Python and ML libraries such as Scikit learn, Pandas, NumPy, TensorFlow, or PyTorch.
• Model training, evaluation, and performance optimization.
• Model registration, versioning, and lifecycle management.
• Production model deployment and CI/CD automation.
• Model monitoring, observability, and performance metrics tracking.
• Github Actions for build, deployment, and automation workflows.
• AWS Cloud Services (2+ years) - Minimum 2 years of experience building and deploying applications on AWS.
• Hands-on experience with: Amazon ECS for container orchestration and application runtime.
• Amazon ECR for container image management.
• Amazon API Gateway for API publishing and routing.
• Amazon RDS for managed relational databases.
• Application Load Balancer (ALB) for traffic management and scaling.
• Amazon S3 for artifact management and object storage.
• Experience implementing secure, scalable, and highly available cloud architectures.
• Backend Development (1+ year) - Minimum 1 year of backend application development experience.
• Experience with: FastAPI based application and service development.
• REST API design, implementation, and documentation.
• SQL programming and relational database concepts.
• PostgreSQL database administration and optimization.
• SQLAlchemy and ORM based data modeling.
• Database schema design and relationship mapping.

Desired qualifications/non essential skills required

• Agentic AI Experience building AI agents, autonomous workflows, or multi-agent systems.
• Familiarity with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.
• Databricks (2+ years) - Experience working with Databricks platform components, including: Unity Catalog for governance and data access management.
• Jobs and Workflows for orchestration and automation.
• Workspace and access management.
• Experience integrating Databricks with enterprise ML and data engineering workflows.

Education

Bachelor's degree in computer science or related field.