Forward Deployed Engineer

Forward Deployed Engineer

The best version of us starts with You! CGI is looking for a Forward Deployed Engineer. With your expertise, you will work with a high performing team to consult and develop solutions for a major client. As a Forward Deployed Engineer (FDE), you will embed directly with the client's teams to take proof of concept (PoC) initiatives the client has already shortlisted and turn them into working software: validating technical feasibility, hardening the solution into a Minimum Viable Product (MVP), running a scoped pilot, and then partnering with the client's Enterprise Architecture team to roll the solution out across the organization.

This position is located on-site in Lafayette, LA (Preferred), Bloomfield, CT, Raleigh, NC or in a Hybrid working Model.

Your Future Duties and Responsibilities

Partner in an embedded, on site/hybrid capacity with the client to take ownership of PoCs the client has already shortlisted and approved for further investment. Perform technical due diligence on each PoC, assessing architecture, data flows, integration points, and the gap between prototype and production grade software. Serve as the primary technical point of contact between the client and CGI, providing regular updates on PoC/MVP/pilot progress, risks, and decisions needed. Partner with CGI's AI/solution architects on solutioning: validating technical approach, leveraging reusable accelerators and best practices, and escalating architecture or design decisions as needed. Scale validated PoCs into MVPs, building in the engineering rigor (testing, CI/CD, monitoring, security controls) required to support real users. Design and execute pilot programs to validate MVPs with a limited user base, gather feedback, and define success criteria and exit conditions. Collaborate closely with the client's Enterprise Architecture team to align each solution's target state architecture, technology standards, and governance requirements. Develop and execute org wide scaling and rollout plans in partnership with Enterprise Architecture, covering migration approach, integration with existing systems, change management, and knowledge transfer. Act as the technical bridge between the client's business/product stakeholders and delivery/engineering teams, translating shortlisted ideas into actionable delivery plans. Identify technical risks, dependencies, and reusable platform components across multiple PoC to scale efforts. Produce documentation, runbooks, and architecture artifacts to support handoff to steady state operations teams. Mentor and support client and delivery teams on best practices for rapid prototyping, MVP engineering, and phased scaling. Design, build, and maintain computer vision pipelines that analyze and extract insights from large volumes of images at scale. Architect and run AI workloads for both training and inference on cloud platforms such as AWS, Azure, or GCP, optimizing for cost, scalability, and performance. Develop automation and tooling using Python for data preprocessing, model training, deployment, and monitoring. Collaborate with cross functional teams to translate complex business problems into machine learning solutions that guide prediction and forecasting. Address the challenges of building, deploying, and scaling production grade computer vision systems, including data quality, model accuracy, latency, and throughput. Monitor model performance in production and implement retraining and continuous improvement (MLOps) workflows.

Required Qualifications to Be Successful in This Role

At least 7+ years of professional software engineering experience in: Hands on experience with at least one major cloud platform (AWS, Azure, or GCP) and modern DevOps practices. Computer vision and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) Building, training, and fine-tuning image processing and deep learning models (classification, detection, segmentation) Python for ML development, data processing, and automation Running AI/ML workloads for training and inference on AWS, Azure, or GCP (e.g., SageMaker, Azure ML, Vertex AI) Processing and managing large scale (TB scale) datasets and their associated data pipelines Building and scaling production ML systems, including MLOps practices such as model deployment, monitoring, and retraining Hands on software/platform engineering experience, including direct experience taking prototypes or PoCs into production. Demonstrated experience scaling a PoC into an MVP and carrying it through a pilot to 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, translating architectural guidance into working implementations.

Good to Have / Bonus Skills: Distributed and multi GPU training and GPU optimization (e.g., CUDA) Containerization and orchestration for ML workloads (Docker, Kubernetes) Large scale data engineering tools (e.g., Spark, Databricks) for image and data processing MLOps tooling (e.g., MLflow, Kubeflow, SageMaker Pipelines) and predictive/forecasting models

Education: Bachelor's degree in computer science or related field.