Role overview
Experience: 35+ years in AI/ML engineering with hands-on deployment experience.
. Technical Skills:
.Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
.Experience with NLP, large language models (LLMs), and generative AI.
.Familiarity with MLOps tools (MLflow, SageMaker, Vertex AI) and containerization (Docker/Kubernetes).
.Strong understanding of data pipelines and integration with APIs.
. Knowledge of cloud platforms (AWS, GCP, Azure) for AI model deployment.
Desired qualifications/non-essential skills required:
. Experience with advanced AI techniques such as RAG (Retrieval-Augmented Generation) and generative AI.
. Exposure to real-time inference and scalable model serving.
. Familiarity with compliance frameworks and security in AI systems.
. Background in performance optimization and cost-efficient AI solutions.
. Experience in large-scale modernization projects in government or regulated environments.
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 - $198,400.00.
CGI Federals benefits are offered to eligible professionals on their first day of employment to include:
Eligibility to participate in an attractive Share Purchase Plan (SPP) in which the company matches dollar-for-dollar contributions made by eligible employees, up to a maximum, for their job category
401(k) Plan and Profit Participation for eligible professionals
Additional benefits determined by your Service Contract Act:
Paid Time Off (PTO)
Paid Federal Holidays
Health & Welfare Benefits
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What you'll work on
.Design and implement AI/ML models for predictive analytics, natural language processing (NLP), and automation.
.Develop and optimize data pipelines for model training and inference.
.Deploy models into production environments using MLOps best practices and tools (e.g., MLflow, Docker, Kubernetes).
.Build APIs and microservices to integrate AI capabilities into enterprise applications.
.Collaborate with data engineers and developers to ensure seamless integration of AI workflows.
.Conduct model validation, performance tuning, and continuous improvement.
.Document AI solutions and ensure compliance with security and governance standards.
What we're looking for
- Continuous Integration