AI/ML Engineer

CGI•Knoxville, TN

3d•$102,000 - $251,100•Hybrid

About The Position

CGI is actively seeking a Senior AI/ML and Emerging Tech Expert to join our Emerging Technologies Practice and lead the design and delivery of AI driven applications across our Federal Portfolio. You will work end to end—from proofs of concept to production—partnering with solution architects, data scientists, and engineering teams to turn ideas into scalable, secure solutions. This role blends full stack engineering with modern AI/ML, integrating generative AI and advanced analytics within Azure, AWS, and GCP to achieve tangible business outcomes. If you are a creative problem solver who thrives in fast moving, highly collaborative environments, come help us push the boundaries of AI and emerging technologies. This position is located in our Knoxville, TN, Lafayette LA, Belton TX, Lebanon VA, Fairfax VA office; however, a hybrid working model is acceptable.

Requirements

US Citizenship is required per contract
Ability to obtain a Secret clearance
Bachelor's and 8 yrs exp required in Computer Science, Software Engineering, Artificial Intelligence & Machine Learning, related field.
5+ years designing, developing, and deploying AI/ML or advanced software solutions.
Proven experience designing and integrating AI/GenAI into products and enterprise applications.
Demonstrated success delivering full stack solutions that embed AI capabilities.
Hands-on experience with GraphDB engineering and development (Neo4j, Amazon Neptune, or similar).
Programming: Python (expert) plus Java
ML/Deep Learning: TensorFlow, PyTorch; model training, tuning, serving, and monitoring.
Generative AI: hands on with LLMs; prompt engineering; fine tuning; RAG patterns; vector databases.
Web & APIs: HTML/CSS/JS; React or Angular; Node.js/Python/Java back ends; REST/GraphQL.
DevOps/MLOps: Docker, Kubernetes, Terraform, AWS CloudFormation; CI/CD with Jenkins/GitLab/Bitbucket; testing frameworks; package managers.
Cloud: Production experience on Azure, AWS, and GCP.
Data: SQL and NoSQL databases; GraphDB; data pipelines; feature stores.
Excellent problem solving, verbal and written communication.
Ability to work independently and within agile teams.
Strong collaboration skills across cross functional stakeholders

Nice To Haves

Advanced degree (Master with 6 years exp /PhD with 3 years exp) in AI/ML or related field.
Certifications in Azure/AWS/GCP; Kubernetes, Terraform, or ML specific certifications are a plus.
Deep proficiency in Generative AI, including RAG, LLM orchestration frameworks, and model lifecycle governance.
Portfolio of successful AI projects; evidence of thought leadership (POCs, publications, OSS contributions).
Familiarity with federal sector standards (e.g., secure SDLC, data governance, compliance frameworks) and industry AI use cases.
Strong client facing skills: solution storytelling, demos, and executive level presentations

Responsibilities

AI/ML Solutioning & Delivery o Design, build, and deploy AI/ML solutions and generative AI applications; architect complex AI pipelines. o Harness large datasets; conduct data mining/analysis; design algorithms and simulations; optimize models for real world performance and cost.
Full Stack Engineering o Implement front end (HTML/CSS/JavaScript; React or Angular) and back end services (Python/Flask/Django; Node.js; Java) that integrate AI capabilities. o Develop robust APIs and microservices; ensure seamless functionality and exceptional UX across platforms.
Cloud & DevSecOps o Deliver against strategic roadmaps for AI and Analytics architectures across Azure, AWS, GCP. o Package and deploy via containerization (Docker, Kubernetes); automate CI/CD (Jenkins, GitLab) and IaC (Terraform, AWS CloudFormation). o Establish best practices and patterns for AI & analytics; implement monitoring, observability, and model governance.
Graph Database Development o Design, implement, and optimize GraphDB solutions (e.g., Neo4j, Amazon Neptune) for AI-driven applications. o Integrate graph-based data models into AI pipelines for knowledge graphs, entity relationships, and semantic search.
Collaboration & Client Engagement o Work closely with data scientists and software engineers to integrate AI into enterprise systems. o Coordinate with client and engineering teams to build, monitor, and maintain solutions. o Support client development opportunities and contribute to thought leadership and demos/POCs