ID
AI Engineering intern at IDEMIA working on algorithms for identity technologies. Involves collaboration with diverse teams and focuses on innovation in biometrics and cryptography.
About the role
- Design and implement LLM‑powered services and agentic workflows (context engineering, memory, tool use/orchestration) for real customer and operations use cases.
- Build and maintain evaluation pipelines (datasets, leaderboards, automated + human evals) to ensure quality, safety, and regression control.
- Productionize models and model APIs on cloud infrastructure (containerization, CI/CD, feature flags, canary releases, observability, incident response).
- Optimize inference for latency, throughput, and cost (batching, caching, prompt/program optimization; use of suitable inference servers).
- Implement retrieval and memory systems (vector search, session/long‑term memory) that improve agent reliability over time.
- Create data flywheels: instrument applications, collect feedback, curate datasets, run targeted fine‑tunes/adaptations, and close the loop with evals.
- Harden systems for security, privacy, and compliance (PII handling, guardrails, content filtering, auditability, documentation).
- Improve developer ergonomics (internal tooling, SDKs, prompt libraries, testing harnesses) to speed up safe experimentation.
- Collaborate cross‑functionally with product, design, and operations; participate in backlog refinement and share learnings across the stack.
Requirements
- Bachelor’s or Master’s in Computer Science/Engineering or equivalent practical experience.
- 6+ years in software/ML engineering delivering production systems with at least +2 years of experience working in complex tech infrastructure.
- Strong Python and OOP; you write clean, testable, maintainable code.
- Experience building backend services/APIs, containers (e.g., Docker), and CI/CD.
- Hands-on integrating hosted and/or open-source LLMs via SDKs or APIs into applications.
- Practical understanding of ML fundamentals (model lifecycle, metrics, failure modes) and experience adapting models or prompts to meet product goals.
- Experience with cloud infrastructure on at least one major provider (AWS/Azure/GCP). AWS experience is a plus but not required.
- Familiarity with retrieval/vector search and evaluation design (offline tests, task design, human-in-the-loop).
- Comfort measuring reliability and performance using any modern observability stack (metrics, logging, tracing).
Benefits
- The chance to build production generative AI at global scale across diverse businesses.
- A collaborative, supportive environment where innovation and initiative are encouraged.
- Modern tooling, mentorship, and clear growth paths across the Prosus portfolio.
- A diverse, inclusive workplace where all voices are heard and valued.
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