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About This Role
- We are looking for an AI / ML Engineer to join our AI group and help build, deploy, and operate reliable, production‑grade AI systems.
- This role is ideal for someone with ML knowledge AND a strong technical foundation who wants to grow within an enterprise environment, working on real‑world AI applications used at scale.
- You will contribute to the full ML lifecycle, collaborating closely with data scientists, developers, and senior hardware engineers to transform research and prototypes into robust, maintainable, and well‑monitored production systems across cloud and on‑prem environments.
- Key Responsibilities: Implementation, deployment, and maintenance of production‑grade ML models, AI agents, and end‑to‑end pipelines.
- Work with data scientists to help translate experimental models into scalable and reliable production solutions.
- Contribute to maintaining and improving ML systems with a focus on performance, scalability, and reliability.
- Follow and contribute to best practices for CI/CD, testing, documentation, and reproducibility.
- Participate in code reviews, design discussions, and post‑deployment reviews to continuously improve system quality.
- What You’ll Gain: Hands‑on experience building production level AI systems.
- Mentorship from senior ML and AI engineers in a structured, supportive environment.
- Exposure to modern AI stacks, including LLMs, agentic workflows, and scalable ML infrastructure.
- Clear growth paths toward Senior AI / ML Engineer/Data Science roles.
Qualifications
- Qualifications: MSc in Computer Science, Statistics, Engineering or related field or BSc with at least 3 years of professional experience working with ML/AI systems.
- Strong Python/C# programming skills.
- Solid understanding of the ML lifecycle, from data preparation and training to deployment, evaluation, and monitoring.
- Experience in LLM pipelines and modern AI frameworks, OR hands‑on exposure through projects or coursework to tools such as LangGraph, LangChain, or LlamaIndex.
- Foundational understanding of RAG concepts, prompt design, and production considerations (latency, cost, reliability).
Nice to Have
- Understanding of production AI use cases such as RAG systems, recommender systems, or LLM‑based services.
- Experience with cloud AI and agentic frameworks.
- Experience working in cross‑functional teams with product, engineering, and data stakeholders.
Sourced directly from Intel’s career page
Your application goes straight to Intel.
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