The PE AI Transformation team at Arm is looking to redefine how AI powers engineering by delivering real-world impact through applied AI, boosting productivity, fueling innovation, and making powerful AI tools part of every engineer’s workflow.
Requirements
- Experience in AI, ML, or software engineering, with validated delivery of production-grade solutions.
- Strong Python programming skills
- Experience deploying AI or ML models into production systems.
- Experience with LLM systems, including prompting, RAG, evaluation, and orchestration.
- Exposure to agentic AI frameworks (e.g., LangChain, LlamaIndex, or custom orchestration stacks).
- Familiarity with enterprise AI APIs (e.g., OpenAI, Anthropic, Azure OpenAI, or similar).
- Experience working with retrieval systems, prompt engineering, and cloud platforms (AWS, Azure, or GCP – AWS preferred)
Responsibilities
- Deliver end-to-end AI solutions, from proof-of-concept to production, that improve developer workflows and engineering productivity.
- Embed with partner teams to find opportunities, translate needs into AI solutions, and deliver tangible, measurable results.
- Implement and optimize LLM-based systems, including retrieval-augmented generation (RAG), evaluation, and guardrails.
- Build and refine agentic AI components such as planning, memory, and tool orchestration.
- Write production-ready, testable Python code, maintaining high standards for quality, security, and performance.
- Collaborate with and learn from senior engineers and architects, applying mentorship feedback to continuously improve design, code quality, and scalability awareness.
- Supply to shared components and documentation, helping establish reusable patterns and frameworks for future projects.
Other
- A learning-oriented mindset—open to feedback, eager to understand architectural trade-offs, and committed to refining your craft.
- Strong communication and collaboration skills, with the ability to partner effectively across disciplines.
- Bachelor's, Master's, or Ph.D. degree in a relevant field (not explicitly mentioned but implied)
- Ability to work in a hybrid environment with flexible working patterns
- Commitment to Arm's equal opportunities and diversity policies
