Senior AI ML Solution Engineer, AI-Native Development

Job Summary

As a Senior AI ML Solution Engineer in the AI-Native Development team, you will design and build AI-powered development pipelines, evaluate ML approaches for code generation and review, and drive the adoption of AI-assisted software development across the organisation.

Key Responsibilities

• Design and build AI-powered development pipelines , from code generation and automated review to feedback loops and evaluation systems.
• Evaluate and select ML approaches for specific problems: when to use LLM prompting vs. fine-tuning (QLoRA), classical ML (random forest, linear regression) vs. reinforcement learning, RAG vs. structured extraction.
• Architect feedback and evaluation systems that measure and improve AI output quality over time.
• Review and refine AI solution architectures , evaluate design decisions, identify weaknesses, propose alternatives with reasoning.
• Lead proof-of-concept development to validate new AI/ML approaches for development tooling.
• Collaborate with the core team to define risk-based development levels and calibrate AI review depth per level.

Requirements

• Hold a M.Sc. or Ph.D. in Computer Science, Electrical or Computer Engineering from a leading university (or equivalent experience).
• 5+ years of industry experience (or equivalent) in AI pipelines architecture or related fields.
• Industry experience building and shipping AI-powered tools or ML pipelines (not just training models , end-to-end delivery).
• Strong understanding of LLM capabilities and limitations , prompt engineering, fine-tuning, RAG, agent architectures.
• Experience with at least two of: reinforcement learning, classical ML, NLP/information retrieval, evaluation framework design.
• Can reason about trade-offs: when to use which approach, with real reasoning backed by shipping experience.
• Strong programming skills (Python required; familiarity with ML frameworks , PyTorch, HuggingFace, etc.).
• Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.

Nice to Have

• Experience with LLM-based code generation, code review, or developer tooling.
• Familiarity with eval frameworks and feedback loop design (online and offline evaluation).
• Experience with AI agent orchestration (multi-agent systems, tool use, planning).
• Shown research track record (publications, open-source contributions).
• Knowledge of AI-assisted development tools and their underlying architectures.