Senior AI Application Developer-Evergreen
- Montreal, QC, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Experience
- 6–10+ years of software engineering experience, including at least 3 years of hands-on experience delivering AI/LLM-powered features in production environments.
- Posting language
- English
- Posted
Original job description
Location Flexibility: Primary Location Only
Req Id: 14081
Posting Start Date: 11/9/26
At Fujitsu, we are driven by our purpose to make the world more sustainable by building trust in society through innovation.
We have been a pioneer in technology and innovation for over 80 years, and we are committed to using our expertise to help businesses and organizations transform for the digital age. We believe that digital transformation is essential to creating a more sustainable future. That's why we are working with our customers to develop solutions that can help them reduce their environmental impact, improve their efficiency, and create a more equitable society.
We are committed to contributing to the United Nations Sustainable Development Goals (SDGs). These goals are a blueprint for a better future for all, and we believe that technology can play a vital role in achieving them.
If you share our passion for making a meaningful impact on the world, we invite you to join our global family of 130,000 employees spanning more than 50 countries. We are a diverse workforce, and we offer a wide range of opportunities for you to grow and develop your career.
Together, we can create a more sustainable future for all.
Key Responsibilities
- Design AI-enabled solution architectures, including RAG, agentic workflows, tool/function calling, and hosted or fine-tuned models.
- Develop backend services, APIs, and frontend integrations to embed AI capabilities into applications.
- Design and optimize prompts, retrieval pipelines, embeddings, context management, and guardrails.
- Establish evaluation and quality assurance frameworks to measure accuracy, reliability, hallucinations, latency, and operational costs.
- Manage the deployment, monitoring, and maintenance of AI solutions in production environments using LLMOps/MLOps practices, CI/CD pipelines, and version control.
- Ensure AI security and responsible AI practices, including data protection, PII handling, access controls, and risk mitigation.
- Collaborate closely with customers and cross-functional teams to ensure compliance with security requirements, established processes, and applicable standards.
- Mentor developers, lead code and design reviews, and establish technical standards and best practices.
- Communicate technical decisions, risks, and trade-offs effectively to both technical and non-technical stakeholders.
- 6–10+ years of software engineering experience, including at least 3 years of hands-on experience delivering AI/LLM-powered features in production environments.
- Strong proficiency in Python and/or other programming languages such as TypeScript, Java, or C#.
- Solid experience in API design, microservices architecture, and cloud-native application development.
- Hands-on experience with Large Language Model (LLM) APIs, including mandatory experience with GitHub Copilot, and familiarity with Anthropic, OpenAI, and open-source models.
- Strong understanding of embeddings, vector databases, RAG pipelines, orchestration frameworks, and agent-based architectures.
- Experience with at least one major cloud platform: AWS, Azure, or GCP.
- Experience with containerization, CI/CD pipelines, and Infrastructure as Code (IaC).
- Strong knowledge of software testing, observability, performance monitoring, and cost optimization.
- Working knowledge of data pipelines, SQL/NoSQL databases, and machine learning fundamentals.
- Ability to work closely with customers to ensure compliance with security requirements and established processes.
Visa Sponsorship Approved: No
At Fujitsu, we are committed to creating a diverse and inclusive workplace where everyone feels valued and respected. We believe that diversity and inclusion are essential to our success, and we are committed to creating an environment where all employees can thrive.
We promote diversity, equal opportunity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, marital status, sexual orientation, gender identity or expression, genetic information, veteran status, or any other characteristic protected by applicable law.
We believe that everyone has something to contribute, and we are committed to creating a workplace where everyone can reach their full potential.
More about this job
Responsibilities
Design and deliver production AI solutions, including RAG pipelines, agentic workflows, APIs, and application integrations, while establishing evaluation, deployment, monitoring, and security practices. Lead technical reviews and standards, mentor developers, and collaborate with customers and cross-functional teams to meet compliance requirements and communicate technical decisions.
Requirements
Requires 6–10+ years of software engineering experience, including at least three years delivering AI/LLM features in production, with strong programming, API, microservices, and cloud-native development skills. Candidates must have hands-on LLM API experience including GitHub Copilot, knowledge of RAG and vector databases, and experience with cloud platforms, CI/CD, containerization, testing, observability, and security compliance.
Skills
- Python
- TypeScript
- Java
- C#
- API Design
- Microservices
- Large Language Models
- GitHub Copilot
- Retrieval-Augmented Generation
- Vector Databases
- Prompt Engineering
- Cloud-Native Development
- AWS
- Azure
- Google Cloud Platform
- CI/CD
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Data & Analytics
- Management & Leadership
Keywords
- Artificial Intelligence
- Large Language Models
- GitHub Copilot
- Anthropic
- OpenAI
- Open-Source Models
- Retrieval-Augmented Generation
- Agentic Workflows
- Tool Calling
- Function Calling
- Embeddings
- Vector Databases
- Prompt Engineering
- Orchestration Frameworks
- Python
- TypeScript
- Java
- C#
- API Design
- Microservices
- AWS
- Azure
- Google Cloud Platform
- Containerization
- CI/CD
- Infrastructure as Code
- LLMOps
- MLOps
- SQL
- NoSQL