Forward Deployed Engineer

The best version of us starts with You!

We CGI looking for 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.

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.

Skills:

• Validation
• Amazon Web Services Cloud
• Azure
• Embedded Software Development
• Enterprise architecture
• Google Cloud Platform
• Stakeholder management
• Systems Architecture
• Artificial Intelligence
• Python
• PyTorch