Manager, AI Solution Engineer

ROLE SUMMARY

As an AI Solution Engineer, you will be a senior technical contributor within the AI Architecture & Solution Design department, supporting the design and delivery of AI-powered business products and reusable capabilities. You will work closely with Embedded AI Solution Architects and the Enterprise AI Capability Design Team to produce solution designs, artefacts, and SDK components that accelerate adoption and scale.

This role is designed as a growth path toward AI Solution Architect. You will blend skills in software engineering, system design, AI engineering, and cloud architecture—not as an expert in all domains, but as a versatile engineer eager to deepen expertise across them. You will contribute to end-to-end solution design for less complex products, assist architects on high-impact initiatives, and help design modular SDK components for enterprise reuse. Over time, you will develop the ability to produce full solution blueprints and guide development teams independently.

Why this Role Matters

AI Solution Engineers are the scaling force behind architecture excellence. By supporting architects and taking ownership of smaller designs, you enable the department to deliver robust, composable, and future-proof AI solutions at speed. You also form the talent pipeline for next-generation AI Solution Architects, ensuring continuity and growth of architectural capability across the enterprise.

ROLE RESPONSIBILITIES

1) Solution Design Support

• Partner with Embedded AI Solution Architects to design solution architectures for prioritized business products; assist in producing artefacts such as:
  • System Context Diagrams
  • Data Flow Diagrams
  • Entity-Relationship Models
  • DB Schema/Relationship Designs
  • Sequence Diagrams
  • Integration Contracts and ADRs
• Support end-to-end design readiness: evaluation coverage, observability plans, security/privacy considerations, documentation completeness, cost optimization and AI workloads resource planning.

2) Ownership of Less Complex Designs

• Act as Solution Designer for smaller or less complex products, producing implementation-ready blueprints aligned to department standards.
• Ensure designs are modular, reusable, and aligned with enterprise architecture principles; maximize leverage of SDKs, modules, and platform services.

3) SDK & Capability Contribution

• Collaborate with the Enterprise AI Capability Design Team to design and implement SDK components and modules for common AI patterns (e.g., RAG, document intelligence, agentic workflows).
• Contribute to API design, integration guides, and code samples; ensure parity between SDK and service implementations.

4) Growth & Skill Development

• Engage in a structured apprenticeship program with rotation opportunities across Embedded Architecture and Capability Design teams to grow platform, technology and tools knowledge.
• Build proficiency in system design, AI engineering, cloud-native architecture, and evaluation frameworks; progressively take on more complex design ownership.
• Participate in design reviews, retrospectives, and knowledge-sharing sessions to accelerate learning.

5) Documentation & Knowledge Management

• Produce clear, complete artefacts for all assigned designs; adhere to documentation standards defined by Technical Writers and Operations Excellence.
• Contribute to pattern libraries and exemplars; ensure designs are indexed and discoverable for reuse.

6) Cross-Team Collaboration

• Work closely with AI Architects, Solution Designers, Software Engineers, and DevOps Engineers to ensure seamless handoff from design to implementation.
• Partner with Platform team on service boundaries, observability, and security readiness for designs that require managed services.

MEASURES OF SUCCESS

• Design quality and completeness: Artefacts meet standards; fewer gaps at architecture review.
• Delivery impact: Assigned designs implemented successfully; reduced rework and improved time-to-first-value.
• Reuse and alignment: Designs leverage SDKs/modules and platform services; minimal bespoke components.
• Skill progression: Demonstrated growth toward AI Solution Architect readiness; positive feedback from mentors and rotation leads.
• Collaboration effectiveness: Strong partner feedback from architects, engineers, and product teams.

QUALIFICATIONS

Basic Qualifications

• 5+ years in software engineering or solution design roles, with exposure to system architecture, cloud-native development, and AI/ML integration.

• Strong proficiency in modern programming languages (e.g., Python, Java, or similar) and familiarity with API design and integration patterns.

• Working knowledge of cloud platforms (Azure, AWS, or GCP) and associated services for compute, storage, and security.

• Ability to produce architecture artefacts (diagrams, ADRs, integration specs) and communicate design decisions clearly.

• Demonstrated growth mindset and willingness to develop expertise across AI engineering, system design, and architecture.

Preferred Qualifications

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

• Familiarity with CI/CD pipelines, observability tooling, and security-by-design principles.

• Exposure to enterprise architecture standards and regulated environments (healthcare, pharma, finance).

• Hands-on experience with data modeling, evaluation frameworks, and performance tuning for AI workloads.

 
Work Location Assignment: Hybrid