Machine Learning Engineer / MLOps Engineer

CGI•Raleigh, NC

8d•Hybrid

About The Position

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.

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

Nice To Haves

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

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.