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About This Role
Position Description:
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CGI is seeking for an expert Full Stack AI Technical Lead for Intelligent Document Processing is responsible for architecting, developing, and delivering end\-to\-end AI\-powered document automation solutions. This role blends deep expertise in AI/ML, LLMs, OCR, NLP, and document understanding with strong full\-stack engineering and cloud architecture skills. You will lead technical strategy, guide engineering teams, and ensure scalable, secure, and high\-quality IDP capabilities across the enterprise.
In this role, you will work closely with cross\-functional teams to identify high\-value use cases and design, prototype, and deliver end\-to\-end AI solutions that directly address those needs using CGI Pulse AI platform. You must be both technically skilled and business\-savvy to translate complex problems into scalable AI\-driven products or tools.
Your future duties and responsibilities:
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AI\-Assisted Software Development
Use GitHub Copilot/Gemini Code Assist/Cloud AI to accelerate code generation, refactoring, and documentation.
Optimize prompts and workflows to improve AI\-generated code quality.
Integrate AI coding assistants into daily development practices.
Monitor and Report Team's efficiency gain in use of AI tools.
Use of AI tool in code review, Quality Assurance
AI \& Document Intelligence
Architect and implement advanced IDP pipelines using OCR, NLP, LLMs, and transformer\-based models.
Lead development of intelligent extraction, classification, summarization, and validation workflows.
Evaluate and integrate document AI platforms (Pulse AI, Google Document AI, etc.).
Design prompt engineering, retrieval\-augmented generation (RAG), and model fine\-tuning strategies.
Full\-Stack Engineering
Build and maintain scalable backend services (Python, Node.js, Java) and modern front\-end applications (React/Angular).
Develop microservices, REST/GraphQL APIs, and event\-driven architectures supporting document workflows.
Ensure high performance, reliability, and maintainability across the entire application stack.
Cloud, DevOps \& AIOps
Architect IDP solution with cloud agnostic technical stack with a deployment capability on Azure/AWS/GCP with strong security and compliance posture.
Implement CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and observability frameworks.
Establish AIOps practices for model deployment, monitoring, retraining, and lifecycle management.
Leadership \& Collaboration
AI\-First delivery mentality. Must be adaptive to develop code using AI tools.
Serve as the technical lead to maintain CGI IDP solution, driving architectural decisions and best practices.
Lead and mentor AI engineers, data scientists, and developers across multiple teams.
Partner with product managers, business stakeholders, and operations teams to define requirements and deliver value.
Conduct code reviews, architecture reviews, and technical roadmap planning.
Lead Design of features and ready to be hands\-on with critical production issues.
Required qualifications to be successful in this role:
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7\+ years of experience in software engineering, with at least 3 years in AI/ML or document intelligence.
Strong proficiency in Python and one or more backend languages (Node.js, Java, Go).
Hands\-on experience with OCR, NLP, LLMs, embeddings, vector databases, and model deployment.
Expertise in cloud platforms (Azure/AWS/GCP) and containerized environments (Docker, Kubernetes).
Proven experience designing and delivering large\-scale, production\-grade systems.
Strong understanding of microservices, distributed systems, and API design
Must Have One end to end production implementation experience with IDP (Intelligent Document Processing) with AIOps.
Preferred Qualifications
Experience with RPA platforms (UiPath, Automation Anywhere, Power Automate).
Familiarity with workflow engines e.g. Airflow.
Knowledge of security frameworks, compliance standards, and enterprise governance.
Background in leading cross\-functional engineering teams.
Experience with RAG pipelines, vector search, and enterprise LLM integration.
Experience on Product Development with knowledge in cost and pricing strategy of delivered products.
Other Information:
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 $125,500\.00 \- $247,100\.00\.
Skills:
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- Artificial Intelligence
- Python
What you can expect from us:
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Together, as owners, lets turn meaningful insights into action.
Life at CGI is rooted in ownership, teamwork, respect and belonging. Here, youll reach your full potential because
You are invited to be an owner from day 1 as we work together to bring our Dream to life. Thats why we call ourselves CGI Partners rather than employees. We benefit from our collective success and actively shape our companys strategy and direction.
Your work creates value. Youll develop innovative solutions and build relationships with teammates and clients while accessing global capabilities to scale your ideas, embrace new opportunities, and benefit from expansive industry and technology expertise.
Youll shape your career by joining a company built to grow and last. Youll be supported by leaders who care about your health and well\-being and provide you with opportunities to deepen your skills and broaden your horizons.
Come join our teamone of the largest IT and business consulting services firms in the world.
Qualified applicants will receive consideration for employment without regard to their race, ethnicity, ancestry, color, sex, religion, creed, age, national origin, citizenship status, disability, pregnancy, medical condition, military and veteran status, marital status, sexual orientation or perceived sexual orientation, gender, gender identity, and gender expression, familial status or responsibilities, reproductive health decisions, political affiliation, genetic information, height, weight, or any other legally protected status or characteristics to the extent required by applicable federal, state, and/or local laws where we do business.
CGI provides reasonable accommodations to qualified individuals with disabilities. If you need an accommodation to apply for a job in the U.S., please email the CGI U.S. Employment Compliance mailbox at US\_Employment\_Compliance@cgi.com. You will need to reference the Position ID of the position in which you are interested. Your message will be routed to the appropriate recruiter who will assist you. Please note, this email address is only to be used for those individuals who need an accommodation to apply for a job. Emails for any other reason or those that do not include a Position ID will not be returned.
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All CGI offers of employment in the U.S. are contingent upon the ability to successfully complete a background investigation. Background investigation components can vary dependent upon specific assignment and/or level of US government security clearance held. Dependent upon role and/or federal government security clearance requirements, and in accordance with applicable laws, some background investigations may include a credit check. CGI will consider for employment qualified applicants with arrests and conviction records in accordance with all local regulations and ordinances.
CGI will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with CGIs legal duty to furnish information.
Salary Context
This $125K-$247K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CGI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($186K) sits 12% above the category median. Disclosed range: $125K to $247K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CGI AI Hiring
CGI has 16 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, Data Engineer. Positions span Knoxville, TN, US, Reston, VA, US, Atlanta, GA, US. Compensation range: $110K - $251K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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