We are seeking a highly autonomous and result-driven AI Software Engineer to transform cutting-edge research from Fujitsu LAB into production-ready Minimum Viable Products (MVPs). This role requires a versatile engineer who can independently drive the entire software development lifecyclefrom understanding high-level product requirements to implementing robust, scalable AI solutions across cloud infrastructure.
The ideal candidate thrives in fast-paced environments with short engineering cycles, can translate abstract requirements into actionable engineering tasks, and has a proven track record of delivering AI/ML products across diverse domains with minimal supervision. The ideal candidate should also be comfortable in working with non-AI products related to computing, security and quantum software.
Key Responsibilities
AI/ML Product Development
- Transform Fujitsu LAB research prototypes and algorithms into production-grade MVPs
- Design, develop, and deploy end-to-end AI/ML applications and services
- Implement and optimize Large Language Models (LLMs) and machine learning pipelines for real-world applications
- Work with advanced AI concepts such as Retrieval-Augmented Generation (RAG), AI agents, and prompt engineering.
- Evaluate and test AI models using theoretical ML knowledge and practical frameworks.
- Create scalable data processing pipelines for AI model training and inference
Full-Stack Development
- Build robust backend services using Python and C++ for high-performance AI workloads
- Develop responsive frontend interfaces using React and JavaScript
- Design and implement RESTful APIs and microservices architectures
- Integrate AI models with web applications and cloud services
Cloud Infrastructure & DevOps
- Architect and deploy cloud-based systems using AWS CloudFormation or similar tools.
- Build and maintain CI/CD pipelines for automated testing, deployment, and monitoring
- Implement DevOps best practices and QA automation frameworks
- Manage containerized applications using Docker and orchestration tools
- Optimize cloud resource utilization and cost efficiency
System Architecture & Security
- Design secure system architectures with proper authentication, authorization, and data protection
- Implement encryption, secure API design, and vulnerability management.
- Ensure system reliability through logging, monitoring, and disaster recovery strategies.
Project Management & Collaboration
- Collaborate with product managers, researchers, and cross-functional teams.
- Participate in Agile ceremonies and contribute to sprint planning and retrospectives.
- Mentor junior engineers and foster a culture of knowledge sharing.
- Conduct code reviews and promote best practices across the team.
Version Control & Software Lifecycle
- Manage source code using GitHub/GitLab with proper branching strategies
- Implement semantic versioning and release management practices
- Maintain clean commit history and meaningful pull requests
- Track issues, features, and technical debt systematically
Required Qualifications
Education & Experience
- Bachelor's or Master's degree in Computer Science, Software Engineering, AI/ML, or related field
- 3-5 years of professional software development experience
- Must have: Completed at least one production deployment of an LLM or machine learning model project
Technical Skills (Must Have)
-
Strong programming proficiency in:
- Python (for AI/ML development, backend services)
- C++ (for performance-critical components)
- JavaScript/React (for frontend development)
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Proven experience with:
- LLM integration and deployment (Hugging Face, open-source models, LLM APIs, etc.)
- Machine learning frameworks (PyTorch, vLLM, scikit-learn)
- Cloud platforms, specifically AWS services
- GitHub/GitLab workflows and Git version control
- CI/CD pipeline design and implementation (GitLab CI, GitHub Actions)
- DevOps practices and QA automation
Experience Requirements
- Participated in multiple large-scale product projects across different domains
- Deep understanding of complete software product lifecycle (planning, development, testing, deployment, maintenance)
- Track record of shipping production AI/ML applications
Essential Soft Skills
- High autonomy: Able to execute complex engineering tasks with minimal technical guidance
- Rapid adaptability: Thrives in fast-paced environments with short development cycles
- Requirement translation: Can independently convert high-level business requirements into detailed technical specifications
- Self-motivated: Strong sense of ownership and accountability
- Problem-solving: Excellent analytical and debugging skills
- Strong communication skills for technical and non-technical audiences
Preferred Qualifications
Additional Technical Skills
- Experience with MLOps tools (MLflow, DVC), model serving (FastAPI, TorchServe).
- Knowledge of confidential computing, TEE, and AI security.
- Understanding of hardware acceleration (GPU, TPU).
- Familiarity with performance profiling and A/B testing frameworks.
- Experience with distributed systems and microservices architecture
