Machine Learning Engineer / MLOps Engineer
Role details
Job location
Tech stack
Job description
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.
This role requires strong expertise in machine learning engineering, backend service development, CI/CD automation, and cloud infrastructure. The candidate will collaborate with data scientists, software engineers, and business stakeholders to deliver production ready AI/ML solutions that drive business value.
This position can be located in Raleigh, NC (Preferred), Lafayette, LA, Bloomfield, CT, Austin, TX in a Hybrid Model.
Your future duties and 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
o MLflow for experiment tracking and model lifecycle management.
o Spark ML and distributed machine learning workflows.
o Python and ML libraries such as Scikit learn, Pandas, NumPy, TensorFlow, or PyTorch.
o Model training, evaluation, and performance optimization.
o Model registration, versioning, and lifecycle management.
o Production model deployment and CI/CD automation.
o Model monitoring, observability, and performance metrics tracking.
o 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
o Amazon ECS for container orchestration and application runtime.
o Amazon ECR for container image management.
o Amazon API Gateway for API publishing and routing.
o Amazon RDS for managed relational databases.
o Application Load Balancer (ALB) for traffic management and scaling.
o 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
o FastAPI based application and service development.
o REST API design, implementation, and documentation.
o SQL programming and relational database concepts.
o PostgreSQL database administration and optimization.
o SQLAlchemy and ORM based data modeling.
o 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:
o Unity Catalog for governance and data access management.
o Jobs and Workflows for orchestration and automation.
o Workspace and access management.
. Experience integrating Databricks with enterprise ML and data engineering workflows.
Education: Bachelor's degree in computer science or related field., * FastAPI
- Amazon Web Services Cloud
- Data Engineering
- Data Engineering
- Databricks
- Machine Learning
- Agentic AI
- Continuous Integration
- Postgre SQL
- RESTful (Rest-APIs)
- SQL
Benefits & conditions
CGI is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. To support the ability to reward for merit-based performance, CGI typically does not hire individuals at or near the top of the range for their role. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $80,600.00 $218,200.00.
CGI's benefits are offered to eligible professionals on their first day of employment to include:
. Competitive compensation
. Comprehensive insurance options
. Matching contributions through the 401(k) plan and the share purchase plan
. Paid time off for vacation, holidays, and sick time
. Paid parental leave
. Learning opportunities and tuition assistance
. Wellness and Well-being programs