Forward Deployed Engineer

CGI•Lafayette, LA

2d•Hybrid

About The Position

CGI is seeking a Forward Deployed Engineer to work with a high-performing team and a major client. Embedded with the client’s teams, the engineer will take shortlisted and approved proof-of-concept initiatives through technical validation, development into a production-ready Minimum Viable Product (MVP), a scoped pilot, and broader organizational rollout in partnership with the client’s Enterprise Architecture team. The role combines client-facing technical leadership with software, platform, and machine-learning engineering. It includes building and operating computer-vision pipelines and cloud-based AI workloads for training and inference, and developing Python automation for data processing, model development, deployment, and monitoring. The position is on-site in Lafayette, Louisiana (preferred), Bloomfield, Connecticut, or Raleigh, North Carolina, or may be performed in a hybrid working model.

Requirements

At least 7 years of professional software engineering experience.
Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP, and modern DevOps practices.
Experience with computer vision and deep-learning frameworks such as PyTorch, TensorFlow, or OpenCV.
Experience building, training, and fine-tuning image-processing and deep-learning models for classification, detection, or segmentation.
Python skills for machine-learning development, data processing, and automation.
Experience running AI/ML training and inference workloads on AWS, Azure, or GCP, such as SageMaker, Azure ML, or Vertex AI.
Experience processing and managing terabyte-scale datasets and associated data pipelines.
Experience building and scaling production ML systems, including MLOps practices for deployment, monitoring, and retraining.
Hands-on software or platform engineering experience, including taking prototypes or PoCs into production.
Demonstrated experience scaling a PoC into an MVP, carrying it through a pilot, and achieving broader production rollout.
Strong full-stack or platform engineering background, including cloud architecture, APIs, data integration, and CI/CD.
Experience working directly with enterprise architecture teams and standards and translating architectural guidance into working implementations.
Bachelor’s degree in computer science or a related field.

Nice To Haves

Distributed and multi-GPU training and GPU optimization, such as CUDA.
Containerization and orchestration for ML workloads, such as Docker and Kubernetes.
Large-scale data engineering tools, such as Spark or Databricks, for image and data processing.
MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines, and predictive or forecasting models.

Responsibilities

Work in an embedded on-site or hybrid capacity with the client, taking ownership of shortlisted and approved PoCs.
Conduct technical due diligence on PoCs, evaluating architecture, data flows, integrations, and the work needed to move prototypes toward production-grade software.
Serve as the primary technical contact between the client and CGI, providing updates on PoC, MVP, and pilot progress, risks, and decisions.
Partner with CGI AI and solution architects to validate technical approaches, use reusable accelerators and best practices, and escalate architecture or design decisions.
Develop validated PoCs into MVPs with testing, CI/CD, monitoring, and security controls suitable for real users.
Design and run pilots with limited user groups, gather feedback, and establish success criteria and exit conditions.
Work with the client’s Enterprise Architecture team to align solutions with target-state architecture, technology standards, and governance requirements.
Plan and execute organization-wide scaling and rollout with Enterprise Architecture, including migration, system integration, change management, and knowledge transfer.
Translate shortlisted business and product ideas into actionable delivery plans, bridging business stakeholders and engineering teams.
Identify technical risks, dependencies, and reusable platform components across PoC-to-scale efforts.
Create documentation, runbooks, and architecture artifacts to support handoff to steady-state operations teams.
Mentor and support client and delivery teams in rapid prototyping, MVP engineering, and phased scaling practices.
Design, build, and maintain computer-vision pipelines that analyze large volumes of images and extract insights at scale.
Architect and operate cloud AI workloads for training and inference, optimizing cost, scalability, and performance.
Develop Python automation and tooling for data preprocessing, model training, deployment, and monitoring.
Collaborate with cross-functional teams to turn business problems into machine-learning solutions for prediction and forecasting.
Address data quality, model accuracy, latency, and throughput challenges in production computer-vision systems.
Monitor production model performance and implement retraining and continuous-improvement MLOps workflows.