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As Manager, Software Engineer (Individual Contributor), you will contribute to the design and development of modular AI capabilities within the Enterprise AI Capability Design Team. Your focus will be on implementing SDK components, interfaces, and supporting modules that enable internal teams and the broader community to integrate AI capabilities seamlessly into applications. You will work under the guidance of senior engineers and architects, gaining exposure to API design, modular architecture, and cloud-native development while progressively taking ownership of components.
You will collaborate closely with:
- Embedded AI Architecture Team and Solution Design Team to ensure SDK interfaces align with real-world solution patterns.
- AI Engineering Team for PI increments and integration of external technologies.
- Digital Creation Centers and Forward Impact Teams (FITs) to capture feedback and improve usability.
This role is designed for engineers with strong software development experience and a growth mindset, eager to deepen expertise in AI engineering, system design, and SDK architecture.
Why this Role Matters
Manager-level Software Engineers are the builders and maintainers of our AI capability SDK. By delivering well-structured, tested, and documented components, you enable reuse, scalability, and developer experience excellence—critical for accelerating AI adoption across the enterprise.
ROLE RESPONSIBILITIES
1) SDK Component Development
- Implement modular SDK components for AI capabilities, including:
- RAG pipeline elements (parsers, chunkers, enrichers, retrievers, connectors)
- Agent and tool cookiecutters with MCP adaptors
- Interfaces for community-driven extensions
- Ensure code adheres to software quality standards for reusability, maintainability, security, readability, and versioning.
2) Testing & Quality Assurance
- Write unit and integration tests for SDK components; collaborate with QA engineers to ensure coverage at various levels (statement / branch / function etc…).
- Integrate logging, metrics, and tracing into SDK components to support observability and troubleshooting.
- Participate in code reviews and maintain compliance with coding standards and security guidelines.
- Contribute to benchmarking and performance validation for new modules.
3) Documentation & Developer Experience
- Produce clear API documentation, usage guides, and code samples for assigned components.
- Support Developer Experience engineers in creating quickstarts and integration guides.
- Ensure documentation aligns with Confluence structure and department standards.
4) Collaboration & Continuous Improvement
- Work closely with Senior Manager, Software Engineer and AI Capability Architecture Lead for design guidance and technical reviews.
- Partner with Embedded AI Architects and Solution Designers to validate SDK interfaces against solution patterns.
- Engage with AI Engineering Team to integrate external technologies and align on PI increments.
5) Growth & Skill Development
- Build proficiency in cloud-native architecture, AI integration patterns, and modular SDK design.
- Participate in knowledge-sharing sessions, retrospectives, and enablement forums.
- Progressively take ownership of more complex components and contribute to architectural discussions.
MEASURES OF SUCCESS
- Component quality: Code meets standards for modularity, maintainability, and performance.
- Testing coverage: High unit/integration test coverage; minimal defects post-release.
- Documentation completeness: Clear API docs, usage guides, and samples delivered with each component.
- Collaboration effectiveness: Positive feedback from senior engineers, architects, and consuming teams.
- Skill progression: Demonstrated growth toward senior-level responsibilities and architectural contributions.
QUALIFICATIONS
Basic Qualifications
- 5+ years in software engineering roles with experience in API development and modular design.
- Proficiency in Python (required) and familiarity with Java or TypeScript.
- Experience with cloud platforms (Azure, AWS, or GCP)and containerized environments.
- Strong understanding of CI/CD pipelines, automated testing, and code quality practices.
- Ability to produce clear documentation and developer-friendly code samples.
- Proficiency in Git-based workflows (feature branches, PR reviews, semantic versioning)
Preferred Qualifications
- Exposure to AI/ML frameworks (PyTorch, TensorFlow) and GenAI/LLM ecosystems (RAG, vector databases, agent frameworks).
- Contribution to open-source projects and/or community-driven extensions.
- Understanding of observability and security-by-design principles.
- Experience with performance tuning and benchmarking for SDK components.
- Work Location Assignment: Hybrid
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