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About This Role
Position Description:
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CGI is a global IT and business consulting services firm delivering end\-to\-end services across consulting, systems integration, and managed services. With thousands of professionals worldwide, CGI partners with clients to drive measurable outcomes through innovation, collaboration, and industry expertise.
We are seeking an experienced AI\-Augmented Engineering Lead to drive modern software delivery through the adoption of AI\-assisted development practices. This role blends deep technical leadership with hands\-on engineering and emerging AI tooling to improve developer productivity, code quality, and delivery speed. You will play a critical role in transforming engineering practices into an AI\-first model while supporting complex insurance domain solutions.
This position serves as the onshore technical anchor within a global delivery model, leading offshore teams while also contributing to business development, pre\-sales activities, and client engagements. Preferred location is Knoxville, TN; however, candidates located near any CGI office will be considered. Eastern Time Zone working hours are required.
Key Responsibilities
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.
Technical Leadership
. Serve as the onshore technical expert for architecture, design, integration patterns, and development quality.
. Provide handson development expertise using Java, RESTEasy, SOAP, XML/JSON, and enterprise integration patterns.
. Lead design reviews, code reviews, and solution walkthroughs to ensure scalability, maintainability, and compliance with standards.
. Perform application performance tuningincluding JVM tuning, thread optimization, profiling, and resolving performance bottlenecks.
. Troubleshoot complex issues across hybrid architectures (onprem \+ cloud).
Insurance Domain Expertise
. Translate insurance\-specific business requirements into scalable technical designs.
. Support solutions related to policy lifecycle, underwriting, and integrations with rating engines and data providers.
. Ensure designs align with insurance industry regulatory, data, and security requirements.
OnshoreOffshore Coordination
. Provide daily technical guidance and clarity to offshore engineers.
. Work closely with offshore leads on sprint planning, task breakdown, quality control, and technical governance.
. Maintain momentum by removing technical blockers and reinforcing engineering standards.
Business Development \& PreSales Support
. Support sales and business development teams with technical insight during RFP/RFI responses and client engagements.
. Deliver technical demonstrations, product walkthroughs, and PoC discussions with prospective clients.
. Assist in crafting architectural proposals, estimates, and solution approaches for new business opportunities.
. Build and maintain demo environments and reusable assets to support sales enablement.
Solution Delivery \& Governance
. Oversee end\-to\-end development from design to deployment.
. Provide hands\-on development support for complex components.
. Ensure solutions meet performance, security, compliance, and insurance domain requirements.
. Drive CI/CD best practices, including automation, testing strategy, and build pipeline reliability.
Stakeholder Communication
. Serve as the primary onshore technical interface for business teams, architects, product owners, and leadership.
. Provide clear, timely updates on delivery status, risks, impacts, and technical decisions.
. Ensure alignment between business objectives, solution architecture, and development execution.
Required Skills \& Experience
Technical Skills
. 1\+ years of experience implementing AI\-assisted development workflows
. 7\+ years of professional software engineering experience, including 2\+ years in a technical lead or team lead role.
. Strong proficiency in Java and development of REST APIs using RESTEasy.
. Deep experience with SOAP services, XML, schemas, and service contracts.
. Strong skill set in performance optimization, including troubleshooting and tuning Java/JVMbased applications.
. Experience working with both on\-prem systems and cloud platforms (AWS or Azure).
. Solid understanding of Git, CI/CD tooling, Maven/Gradle, Jenkins, and automated test frameworks.
. Familiarity with relational databases and SQL fundamentals (e.g., SQL Server, PostgreSQL).
Insurance Industry Background
. Experience delivering solutions in one or more insurance domains:
. Understanding of insurance data flows, regulatory considerations, and thirdparty data sources.
Leadership \& Collaboration
. Experience guiding or coordinating offshore/remote development teams.
. Excellent communication and clientfacing presentation skills.
. Ability to mentor developers, enforce coding standards, and uphold engineering discipline.
. Proficiency in Agile/Scrum methodologies.
Preferred Qualifications
. AWS, Google or Azure cloud developer certifications
. Experience with Docker, Kubernetes, or other container technologies.
. Experience with caching tools such as Redis or Elasticache.
. Experience with data science\-adjacent languages like Python or R.
. Familiarity with API gateways (Apigee, Kong, MuleSoft, Azure API Management).
. Knowledge of microservices, event\-driven frameworks, and messaging platforms (Kafka, JMS, RabbitMQ).
. Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience).
Success Factors
The ideal candidate will:
. Confidently lead technical efforts while supporting offshore development execution.
. Communicate clearly and effectively with clients, stakeholders, and delivery teams.
. Be comfortable in pre\-sales scenariosdemonstrating solutions and influencing technical decisions.
. Possess strong insurance domain understanding and the ability to architect solutions around industryspecific processes.
. Balance hands\-on problem solving with strategic technical leadership.
Your future duties and responsibilities:
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How you'll make an impact
Leverage AI coding assistants such as GitHub Copilot and Gemini Code Assist to accelerate development, refactoring, and documentation
Optimize prompt engineering techniques and workflows to enhance AI\-generated code quality and team productivity
Integrate AI\-assisted development practices into daily engineering workflows and software delivery lifecycle
Serve as the onshore technical leader for architecture, design patterns, and enterprise integration solutions
Lead hands\-on development efforts using Java, RESTEasy, SOAP, and modern API frameworks
Conduct code reviews, design reviews, and technical walkthroughs to ensure scalability and maintainability
Drive performance optimization efforts including JVM tuning, profiling, and resolving bottlenecks
Coordinate closely with offshore teams to guide sprint execution, remove blockers, and enforce engineering standards
Support pre\-sales efforts through technical demos, solution design, and proposal development
Ensure end\-to\-end delivery aligns with performance, security, compliance, and insurance domain requirements
Required qualifications to be successful in this role:
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What you'll bring
7\+ years of software engineering experience with at least 2\+ years in a technical or team leadership role
1\+ years of experience implementing AI\-assisted development tools and workflows in enterprise environments
Strong proficiency in Java and REST API development using RESTEasy
Hands\-on experience with SOAP services, XML, schemas, and service contract design
Proven expertise in performance tuning and troubleshooting Java/JVM\-based applications
Experience working in hybrid environments including on\-premise systems and cloud platforms (AWS or Azure)
Strong knowledge of CI/CD tools, Git, Maven/Gradle, Jenkins, and automated testing frameworks
Solid understanding of relational databases and SQL (e.g., SQL Server, PostgreSQL)
Experience working within insurance domains such as policy administration, underwriting, or rating systems
Strong communication skills with the ability to engage clients, stakeholders, and distributed teams effectively
Desired qualifications
Cloud certifications in AWS, Azure, or Google Cloud Platform
Experience with containerization technologies such as Docker and Kubernetes
Familiarity with caching technologies like Redis or ElastiCache
Exposure to Python, R, or other data\-oriented programming languages
Experience with API gateways such as Apigee, Kong, or MuleSoft
Knowledge of microservices, event\-driven architectures, and messaging platforms like Kafka or RabbitMQ
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 $102,000\.00 \- $218,200\.00\.
CGI's benefits are offered to eligible professionals on their first day of employment to include:
. Competitive compensation
. Comprehensive insurance options
. Matching contributions through the 401(k) plan and the share purchase plan
. Paid time off for vacation, holidays, and sick time
. Paid parental leave
.Learning opportunities and tuition assistance
. Wellness and Well\-being programs
Skills:
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- Artificial Intelligence
- Cloud Computing
- Insurance
- Java
- Performance Tuning
- RESTful (Rest\-APIs)
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.
We make it easy to translate military experience and skills! to be directed to our site that is dedicated to veterans and transitioning service members.
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 $102K-$218K range is below the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At CGI, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 32% below the category median. Disclosed range: $102K to $218K.
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
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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