As Senior Manager, Software Engineer (Individual Contributor), you will be a senior technical authority within the Enterprise AI Capability Design Team, responsible for designing and implementing modular, reusable AI capabilities in the form of SDKs and associated components. Your mission is to build developer-first, extensible SDKs that enable internal teams and the broader community to integrate AI capabilities seamlessly into applications, while maintaining consistency, quality, and performance.

You will design and implement core modules for RAG pipelines, agent and tool cookiecutters, MCP adaptors, and standardized interfaces that allow community-driven extensions. You will also collaborate on automated pipelines for code quality checks, benchmarking, and integration tests, ensuring every capability meets enterprise-grade standards for reliability, security, and observability.

This role requires close collaboration with:

• Embedded AI Architecture Team and Solution Design Team to prioritize capabilities and define SDK interfaces aligned to real-world solution patterns.

• AI Engineering Team, which actively develops PI increments of the SDK and integrates external technologies.

• Digital Creation Centers and Forward Impact Teams (FITs), who will consume these capabilities in product delivery and provide feedback for continuous improvement.

Why this Role Matters

This role is the engineering backbone of our AI capability strategy. By delivering high-quality SDKs and reusable modules, you enable rapid adoption, reduce bespoke engineering, and ensure parity between SDK and service implementations. Your work directly impacts developer experience, scalability, and the ability to operationalize AI capabilities across the enterprise.

ROLE RESPONSIBILITIES

1) SDK & Module Development

• Design and implement modular SDK components for AI capabilities, including:

  • RAG pipeline modules (parsers, chunkers, enrichers, retrievers, rankers, connectors)

  • Cookiecutters for agents and tools with MCP adaptors

  • Standardized interfaces for community-driven extensions

• Ensure SDKs are extensible, composable, and versioned with clear upgrade paths.

2) Collaboration & Interface Definition

• Work closely with Embedded AI Architects and Solution Designers to define SDK interfaces and design patterns based on prioritized use cases.

• Partner with AI Engineering Team to integrate external technologies, validate new components, and align on PI increments for SDK evolution.

• Engage with Digital Creation Centers and FITs to capture feedback on usability, performance, and integration experience.

3) Code Quality & Testing Automation

• Collaborate with Evaluation & QA Engineers to integrate unit, integration, and performance tests into CI/CD pipelines.

• Implement automated code quality checks, linting, and security scans for SDK components.

• Contribute to benchmarking frameworks for new modules and features.

4) Developer Experience & Enablement

• Work closely with Developer Experience Engineers to deliver clear APIs, ergonomic SDK design, and developer-friendly documentation.

• Provide quickstarts, code samples, and integration guides to accelerate adoption.

• Participate in community enablement efforts, ensuring SDK interfaces are intuitive and extensible.

5) Architecture & Standards Alignment

• Align SDK design with enterprise architecture principles, ensuring parity with service implementations.

• Collaborate with AI Capability Architecture Lead on interface contracts, observability hooks, and security-by-design patterns.

• Maintain semantic versioning and backward compatibility policies for SDK releases.

MEASURES OF SUCCESS

• SDK quality and adoption: High developer satisfaction; increased reuse across products; minimal bespoke implementations.

• Release health: Predictable release cadence; automated quality gates; successful integration tests.

• Performance and reliability: SDK modules meet latency, throughput, and cost targets; observability hooks implemented.

• Community engagement: Positive feedback on extensibility and contribution workflows; active feature branch participation.

• Documentation completeness: Clear quickstarts, code samples, and integration guides delivered with each release.

QUALIFICATIONS

Basic Qualifications

• 7+ years in software engineering roles with strong experience in API/SDK design and modular architecture.

• Proficiency in modern programming languages (Python required).

• Hands-on experience with cloud-native development (Azure, AWS, or GCP) and containerized environments.

• Strong understanding of CI/CD pipelines, automated testing, and code quality practices.

• Ability to produce developer-friendly SDKs with clear documentation and ergonomic interfaces.

• Proficiency in Git-based workflows (feature branches, PR reviews, semantic versioning)

Preferred Qualifications

• Experience with AI/ML frameworks (PyTorch, TensorFlow) and GenAI/LLM ecosystems (prompt tooling, RAG, vector databases, agent frameworks).

• Contribution to open-source projects and/or community-driven extensions.

• Exposure to observability tooling (metrics, tracing, logging) and security-by-design principles.

• Background in performance engineering and benchmarking for AI workloads.

• Experience in regulated environments with audit-ready documentation and compliance standards.